Citations to Publications ofDr. Carlos A. Coello Coello

that appear in the ISI Web of Science.The total of citations (excluding self-citations and citations from his co-authors)

is 5937.

Tesis Doctoral

• Carlos A. Coello Coello. An Empirical Study of Evolutionary Techniques for Multiobjective Optimizationin Engineering Design, PhD thesis, Department of Computer Science, Tulane University, New Orleans,Louisiana, April 1996.

1. Nadia Nedjah and Luiza de Macedo Mourelle, “Evolutionary multi-objective optimisation: a survey”, InternationalJournal of Bio-Inspired Computation, Vol. 7, No. 1, pp. 1–25, 2015.

2. Ahmad Mozaffari, Mofid Gorji-Bandpy, Pendar Samadian, Rouzbeh Rastgar and Alireza Rezania Kolaei, “Compre-hensive preference optimization of an irreversible thermal engine using pareto based mutable smart bee algorithm andgeneralized regression neural network”, Swarm and Evolutionary Computation, Vol. 9, pp. 90–103, April 2013.

3. Ayeley P. Tchangani, “Considering Bipolarity of Attributes With Regards to Objectives in Decisions Evaluation”,Inzinerine Ekonomika–Engineering Economics, Vol. 21, No. 5, pp. 475–484, 2010.

4. K. Metaxiotis and K. Liagkouras, “Multiobjective Evolutionary Algorithms for Portfolio Management: A comprehensiveliterature review”, Expert Systems with Applications, Vol. 39, No. 14, pp. 11685–11698, October 15, 2012.

5. Musrrat. Ali, Patrick Siarry and Millie. Pant, “An efficient Differential Evolution based algorithm for solving multi-objective optimization problems”, European Journal of Operational Research, Vol. 217, No. 2, pp. 404–416, March 1,2012.

6. Hamit Saruhan, “Pivoted-pad journal bearings lubrication design”, Industrial Lubrication and Tribology, Vol. 63, Nos.2-3, pp. 119–126, 2011.

7. Mark A. Gammon, “Optimization of fishing vessels using a Multi-Objective Genetic Algorithm”, Ocean Engineering,Vol. 38, No. 10, pp. 1054–1064, July 2011.

8. Cleber Zanchettin, Teresa B. Ludermir and Leandro Maciel Almeida, “Hybrid Training Method for MLP: Optimizationof Architecture and Training”, IEEE Transactions on Systems, Man, and Cybernetics Part B–Cybernetics, Vol. 41, No.4, pp. 1097–1109, August 2011.

9. Min-Yuan Cheng and Ching-Shan Chen, “Optimal planning model for school buildings considering the tradeoff of seismicresistance and cost effectiveness: a Taiwan case study”, Structural and Multidisciplinary Optimization, Vol. 43, No. 6,pp. 863–879, June 2011.

10. Indika Meedeniya, Barbora Buhnova, Aldeida Aleti and Lars Grunske, “Reliability-driven deployment optimization forembedded systems”, Journal of Systems and Software, Vol. 84, No. 5, pp. 835–846, May 2011.

11. S. Dhouib, A. Kharrat and H. Chabchoub, “Goal programming using multiple objective hybrid metaheuristic algorithm”,Journal of the Operational Research Society, Vol. 62, No. 4, pp. 677–689, April 2011.

12. Souhail Dhouib, Aida Kharrat and Habib Chabchoub, “A multi-start threshold accepting algorithm for multiple objectivecontinuous optimization problems”, International Journal for Numerical Methods in Engineering, Vol. 83, No. 11, pp.1498–1517, September 10, 2010.

13. C.A. Cortes, E. Mombello, R. Dib and G. Ratta, “A new class of flat-top windows for exposure assessment in magneticfield measurements”, Signal Processing, Vol. 87, No. 9, pp. 2151–2164, September 2007.

14. Wahed Mohamed, Ibrahim Wesam and Effat Ahmed, “Finding an optimization of the plate element of Egyptian researchreactor using genetic algorithm”, Nuclear Science and Techniques, Vol. 19, No. 5, pp. 314–320, October 20, 2008.

15. Boguslaw Pytlak, “Multicriteria optimization of hard turning operation of the hardened 18HGT steel”, InternationalJournal of Advanced Manufacturing Technology, Vol. 49, Nos. 1–4, pp. 305–312, July 2010.

16. J. Dipama, A. Teyssedou, F. Aube and L. Lizon-A-Lugrin, “A grid based multi-objective evolutionary algorithm for theoptimization of power plants”, Applied Thermal Engineering, Vol. 30, Nos. 8-9, pp. 807–816, June 2010.

17. Zhi-Hua Hu, “A multiobjective immune algorithm based on a multiple-affinity model”, European Journal of OperationalResearch, Vol. 202, No. 1, pp. 60–72, April 1, 2010.

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18. Honglin Li, Hailei Zhang, Mingyue Zheng, Jie Luo, Ling Kang, Xiaofeng Liu, Xicheng Wang and Hualiang Jiang, “Aneffective docking strategy for virtual screening based on multi-objective optimization algorithm”, BMC Bioinformatics,Vol. 10, article number 58, February 11, 2009.

19. A. Albers, N. Leon-Rovira, H. Aguayo and T. Maier, “Development of an engine crankshaft in a framework of computer-aided innovation”, Computers in Industry, Vol. 60, No. 8, pp. 604–612, October 2009.

20. Mohamed El-Sayed Wahed, Wesam Zakaria Ibrahim and Ahmed Mostafa Effat, “Multiobjective Optimization of thePlate Element of Egyptian Research Reactor Using Genetic Algorithm”, Nuclear Science and Engineering, Vol. 162, No.3, pp. 275–281, July 2009.

21. Min-Rong Chen, Yong-zai Lu and Gen-ke Yang, “Multiobjective extremal optimization with applications to engineeringdesign”, Journal of Zhejiang University SCIENCE A, Vol. 8, No. 12, pp. 1905–1911, November 2007.

22. C. Elegbede, “Structural reliability assessment based on particles swarm optimization”, Structural Safety, Vol. 27, No.2, pp. 171–186, 2005.

23. Adil Baykasoglu, “Preemptive goal programming using simulated annealing”, Engineering Optimization, Vol. 37, No. 1,pp. 49–63, January 2005.

24. Guan-Chun Luh and Chung-Huei Chueh, “Multi-objective optimal design of truss structure with immune algorithm”,Computers & Structures, Vol. 82, Nos. 11–12, pp. 829–844, May 2004.

25. J. Oh and C. Wu, “Genetic-algorithm-based real-time task scheduling with multiple goals”, Journal of Systems andSoftware, Vol. 71, No. 3, pp. 245–258, May 2004.

26. C. Elegbede and K. Adjallah, “Availability allocation to repairable systems with genetic algorithms: a multi-objectiveformulation”, Reliability Engineering & System Safety, Vol. 82, No. 3, pp. 319–330, December 2003.

27. Balram Suman, “Simulated Annealing-Based Multiobjective Algorithms and Their Application for System Reliability”,Engineering Optimization, Vol. 35, No. 4, pp. 391–416, August 2003.

28. R.F. Coelho, H. Bersini and P. Bouillard, “Parametrical mechanical design with constraints and preferences: applicationto a purge valve”, Computer Methods in Applied Mechanics and Engineering, Vol. 192, Nos. 39–40, pp. 4355–4378,2003.

29. B. De Smedt and G.C.E. Gielen, “WATSON: Design space boundary exploration and model generation for analog andRF IC design”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 22, No. 2, pp.213–224, February 2003.

30. Johan Andersson and David Wallace, “Pareto optimization using the struggle genetic crowding algorithm”, EngineeringOptimization, Vol. 34, No. 6, pp. 623–643, December 2002.

31. Guan-Chun Luh, Chung-Huei Chueh and Wei-Wen Liu, “MOIA: Multi-Objective Immune Algorithm”, EngineeringOptimization, Volume 35, No. 2, pp. 143–164, April 2003.

32. K.C. Tan, E.F. Khor, T.H. Lee and Y.J. Yang, “A tabu-based exploratory evolutionary algorithm for multiobjectiveoptimization”, Artificial Intelligence Review, Vol. 19, No. 3, pp. 231–260, May 2003.

33. K.C. Tan, E.F. Khor, T.H. Lee and R. Sathikannan, “An evolutionary algorithm with advanced goal and priorityspecification for multi-objective optimization”, Journal of Artificial Intelligence Research, Vol. 18, pp. 183–215, 2003.

34. K.C. Tan, T.H. Lee and E.F. Khor, “Evolutionary Algorithms for Multi-Objective Optimization: Performance Assess-ments and Comparisons”, Artificial Intelligence Review, Vol. 17, No. 4, pp. 253–290, June 2002.

35. K.C. Tan, T.H. Lee & E. F. Khor, “Evolutionary Algorithms with Dynamic Population Size and Local Exploration forMultiobjective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 5, No. 6, pp. 565-588, December2001.

36. A. Baykasoglu, “Goal programming using multiple objective tabu search”, Journal of the Operational Research Society,Vol. 52, No. 12, pp. 1359–1369, December 2001.

37. K.C. Tan, Tong H. Lee, D. Khoo & E.F. Khor, “A Multiobjective Evolutionary Algorithm Toolbox for Computer-AidedMultiobjective Optimization”, IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, Vol. 31,No. 4, pp. 537–556, August 2001.

38. Johan Andersson and Peter Krus, “Multiobjective Optimization of Mixed Variable Design Problems”, en Eckart Zitzler,Kalyanmoy Deb, Lothar Thiele, Carlos A. Coello Coello & David Corne (Eds.), First International Conference onEvolutionary Multi-Criterion Optimization, Springer-Verlag, Zurich, Suiza, pp. 624–638, Marzo de 2001.

39. Matthias Ehrgott and Xavier Gandibleux, “A Survey and Annotated Bibliography of Multiobjective CombinatorialOptimization”, OR Spektrum, Vol. 22, pp. 425–460, 2000.

40. B. Suman, “Study of self-stopping PDMOSA and performance measure in multiobjective optimization”, Computers &Chemical Engineering, Vol. 29, No. 5, pp. 1131–1147, April 15, 2005.

41. J. Olvander, “Robustness considerations in multi-objective optimal design”, Journal of Engineering Design, Vol. 16, No.5, pp. 511–523, October 2005.

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42. M. Omran, A.P. Engelbrecht and A. Salman, “Particle swarm optimization method for image clustering”, InternationalJournal of Pattern Recognition and Artificial Intelligence, Vol. 19, No. 3, pp. 297–321, May 2005.

43. David Greiner, Gabriel Winter, Jose M. Emperador and Blas Galvan, “Gray Coding in Evolutionary MulticriteriaOptimization: Application in Frame Structural Optimum Design”, in Carlos A. Coello Coello, Arturo Hernandez Aguirreand Eckart Zitzler (editors), Evolutionary Multi-Criterion Optimization. Third International Conference, EMO 2005,pp. 576–591, Springer. Lecture Notes in Computer Science Vol. 3410, Guanajuato, Mexico, March 2005.

44. Seyed Hamid Reza Pasandideh and Seyed Taghi Akhavan Niaki, “Multi-response simulation optimization using geneticalgorithm within desirability function framework”, Applied Mathematics and Computation, Vol. 175, No. 1, pp. 366–382,April 1, 2006.

45. B. Suman and P. Kumar, “A survey of simulated annealing as a tool for single and multiobjective optimization”, Journalof the Operational Research Society, Vol. 57, No. 10, pp. 1143–1160, October 2006.

Libros

• Carlos A. Coello Coello, David A. Van Veldhuizen and Gary B. Lamont, “Evolutionary Algorithms for SolvingMulti-Objective Problems”, Kluwer Academic Publishers, New York, USA, ISBN 0-3064-6762-3, May 2002.o Carlos A. Coello Coello, Gary B. Lamont and David A. Van Veldhuizen, “Evolutionary Algorithms forSolving Multi-Objective Problems”, Second Edition, Springer-Verlag, New York, USA, Septiembre 2007,ISBN 978-0-387-33254-3.

1. Leonardo C.T. Bezerra,Manuel Lopez-Ibanez and Thomas Stutzle, “Automatic Component-Wise Design of Multiobjec-tive Evolutionary Algorithms”, IEEE Transactions on Evolutionary Computation, Vol. 20, No. 3, pp. 403–417, June2016.

2. Murilo Zangari, Alexander Mendiburu, Roberto Santana and Aurora Pozo, “Multiobjective decomposition-based MallowsModels estimation of distribution algorithm. A case of study for permutation flowshop scheduling problem”, InformationSciences, Vol. 397, pp. 137–154, August 2017.

3. Yanan Sun, Gary G. Yen and Zhang Yi, “Reference line-based Estimation of Distribution Algorithm for many-objectiveoptimization”, Knowledge-Based Systems, Vol. 132, pp. 129–143, September 15, 2017.

4. Masoud Afrand, Said Farahat, Alireza Hossein Nezhad, Ghanbar Ali Sheikhzadeh, Faramarz Sarhaddi and SomchaiWongwises, “Multi-objective optimization of natural convection in a cylindrical annulus mold under magnetic field usingparticle swarm algorithm”, International Communications in Heat and Mass Transfer, Vol. 60, pp. 13–20, January2015.

5. Xinye Cai, Zhixiang Yang, Zhun Fan and Qingfu Zhang, “Decomposition-Based-Sorting and Angle-Based-Selection forEvolutionary Multiobjective and Many-Objective Optimization”, IEEE Transactions on Cybernetics, Vol. 47, No. 9,pp. 2824–2837, September 2017.

6. Luis Lobato Macedo, Pedro Godinho and Maria Joao Alves, “Mean-semivariance portfolio optimization with multiobjec-tive evolutionary algorithms and technical analysis rules”, Expert Systems with Applications, Vol. 79, pp. 33–43, August15, 2017.

7. Helio Freire, P.B. Moura Oliveira and E.J. Solteiro Pires, “From Single to Many-objective PID Controller Design usingParticle Swarm Optimization”, International Journal of Control Automation and Systems, Vol. 15, No. 2, pp. 918–932,April 2017.

8. Aurora Ramirez, Jose Antonio Parejo, Jose Raul Romero, Sergio Segura and Antonio Ruiz-Cortes, “Evolutionary com-position of QoS-aware web services: A many-objective perspective”, Expert Systems with Applications, Vol. 72, pp.357–370, April 15, 2017.

9. Laura Cruz-Reyes, Eduardo Fernandez and Nelson Rangel-Valdez, “A metaheuristic optimization-based indirect elicita-tion of preference parameters for solving many-objective problems”, International Journal of Computational IntelligenceSystems, Vol. 10, No. 1, pp. 56–77, January 2017.

10. Yi Xiang, Yuren Zhou, Miqing Li and Zefeng Chen, “A Vector Angle-Based Evolutionary Algorithm for UnconstrainedMany-Objective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 21, No. 1, pp. 131–152, Febru-ary 2017.

11. Eduardo Segredo, Carlos Segura and Coromoto Leon, “Memetic algorithms and hyperheuristics applied to a multi-objectivised two-dimensional packing problem”, Journal of Global Optimization, Vol. 58, No. 4, pp. 769–794, April2014.

12. Antonio Gaspar-Cunha, Jose Ferreira and Gustavo Recio, “Evolutionary robustness analysis for multi-objective opti-mization: benchmark problems”, Structural and Multidisciplinary Optimization, Vol. 49, No. 5, pp. 771–793, May2014.

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13. Yongpeng Shen and Yaonan Wang, “Operating Point Optimization of Auxiliary Power Unit Using Adaptive Multi-Objective Differential Evolution Algorithm”, IEEE Transactions on Industrial Electronics, Vol. 64, No. 1, pp. 115–124,January 2017.

14. Yi-xin Su and Rui Chi, “Multi-objective particle swarm-differential evolution algorithm”, Neural Computing & Applica-tions, Vol. 28, No. 2, pp. 407–418, February 2017.

15. T. Lust and D. Tuyttens, “Variable and large neighborhood search to solve the multiobjective set covering problem”,Journal of Heuristics, Vol. 20, No. 2, pp. 165–188, April 2014.

16. Cristobal J. Carmona, Pedro Gonzalez, Maria Jose del Jesus and Francisco Herrera, “Overview on evolutionary sub-group discovery: analysis of the suitability and potential of the search performed by evolutionary algorithms”, WileyInterdisciplinary Reviews–Data Mining and Knowledge Discovery, Vol. 4, No. 2, pp. 87–103, March 2014.

17. Z.M. Nopiah, M.H. Osman, S. Abdullah and M.N. Baharin, “Application of a Multi-Objective Approach and SequentialCovering Algorithm to the Fatigue Segment Classification Problem”, Arabian Journal for Science and Engineering, Vol.39, No. 3, pp. 2165–2177, March 2014.

18. Arnaud Liefooghe, Sebastien Verel and Jin-Kao Hao, “A hybrid metaheuristic for multiobjective unconstrained binaryquadratic programming”, Applied Soft Computing, Vol. 16, pp. 10–19, March 2014.

19. Stefanos V. Papaefthymiou and Stavros A. Papathanassiou, “Optimum sizing of wind-pumped-storage hybrid powerstations in island systems”, Renewable Energy, Vol. 64, pp. 187–196, April 2014.

20. Weihua Zhang and Marc Reimann, “A simple augmented epsilon-constraint method for multi-objective mathematicalinteger programming problems”, European Journal of Operational Research, Vol. 234, No. 1, pp. 15–24, April 1, 2014.

21. Yu Lei, Maoguo Gong, Jun Zhang, Wei Li and Licheng Jiao, “Resource allocation model and double-sphere crowdingdistance for evolutionary multi-objective optimization”, European Journal of Operational Research, Vol. 234, No. 1, pp.197–208, April 1, 2014.

22. Fernando Jimenez, Gracia Sanchez and Jose M. Juarez, “Multi-objective evolutionary algorithms for fuzzy classificationin survival prediction”, Artificial Intelligence in Medicine, Vol. 60, No. 3, pp. 197–219, March 2014.

23. Rui Wang, Peter J. Fleming and Robin C. Purshouse, “General framework for localised multi-objective evolutionaryalgorithms”, Information Sciences, Vol. 258, pp. 29–53, February 10, 2014.

24. Cai Dai and Yuping Wang, “A New Multiobjective Evolutionary Algorithm Based on Decomposition of the ObjectiveSpace for Multiobjective Optimization”, Journal of Applied Mathematics, Article Number: 906147, 2014.

25. Wei-Yu Chiu, Gary G. Yen and Teng-Kuei Juan, “Minimum Manhattan Distance Approach to Multiple Criteria DecisionMaking in Multiobjective Optimization Problems”, IEEE Transactions on Evolutionary Computation, Vol. 20, No. 6,pp. 972–985, December 2016.

26. Bin Xu, Rongbin Qi, Weimin Zhong, Wenli Du and Feng Qian, “Optimization of p-xylene oxidation reaction processbased on self-adaptive multi-objective differential evolution”, Chemometrics and Intelligent Laboratory Systems, Vol.127, pp. 55–62, August 15, 2013.

27. Rui Wang, Qingfu Zhang and Tao Zhang, “Decomposition-Based Algorithms Using Pareto Adaptive Scalarizing Meth-ods”, IEEE Transactions on Evolutionary Computation, Vol. 20, No. 6, pp. 821–837, December 2016.

28. Parviz Fattahi, Vahid Hajipour and Arash Nobari, “A bi-objective continuous review inventory control model: Pareto-based meta-heuristic algorithms”, Applied Soft Computing, Vol. 32, pp. 211–223, July 2015.

29. Manuel Barraza, Eden Bojorquez, Eduardo Fernandez-Gonzalez and Alfredo Reyes-Salazar, “Multi-objective Optimiza-tion of Structural Steel Buildings under Earthquake Loads using NSGA-II and PSO”, KSCE Journal of Civil Engineering,Vol. 21, No. 2, pp. 488–500, February 2017.

30. Abraham Duarte, Juan J. Pantrigo, Edugardo G. Pardo and Nenad Mladenovic, “Multi-objective variable neighborhoodsearch: an application to combinatorial optimization problems”, Journal of Global Optimization, Vol. 63, No. 3, pp.515–536, November 2015.

31. Pezhman Sharafi, Lip H. Teh and Muhammad N.S. Hadi, “Conceptual design optimization of rectilinear building frames:A knapsack problem approach”, Engineering Optimization, Vol. 47, No. 10, pp. 1303–1323, October 3, 2015.

32. Hu Zhang, Shenmin Song, Aimin Zhou and X.Z. Gao, “A multiobjective cellular genetic algorithm based on 3D structureand cosine crowding measurement”, International Journal of Machine Learning and Cybernetics, Vol. 6, No. 3, pp. 487–500, June 2015.

33. Xin Qiu, Jian-Xin Xu, Kay Chen Tan and Hussein A. Abbass, “Adaptive Cross-Generation Differential EvolutionOperators for Multiobjective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 20, No. 2, pp.232–244, April 2016.

34. Sandra M. Venske, Richard A. Goncalves, Elaine M. Benelli and Myriam R. Delgado, “ADEMO/D: An adaptive dif-ferential evolution for protein structure prediction problem”, Expert Systems with Applications, Vol. 56, pp. 209–226,September 1, 2016.

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35. Kaname Narukawa, Yu Setoguchi, Yuki Tanigaki, Markus Olhofer, Bernhard Sendhoff and Hisao Ishibuchi, “Preferencerepresentation using Gaussian functions on a hyperplane in evolutionary multi-objective optimization”, Soft Computing,Vol. 20, No. 7, pp. 2733–2757, July 2016.

36. Xiaoliang Ma, Fang Liu, Yutao Qi, Lingling Li, Licheng Jiao, Xiaozheng Deng, Xiaodong Wang, Bei Dong, ZhantingHou, Yongxiao Zhang and Jianshe Wu, “MOEA/D with biased weight adjustment inspired by user preference and itsapplication on multi-objective reservoir flood control problem”, Soft Computing, Vol. 20, No. 12, pp. 4999–5023,December 2016.

37. Rajan Filomeno Coelho, “Bi-objective hypervolume-based Pareto optimization A gradient-based approach as an alter-native to evolutionary algorithms”, Optimization Letters, Vol. 9, No. 6, pp. 1091–1103, August 2015.

38. B. Rosario Campomanes-Alvarez, Oscar Cordon and Sergio Damas, “Evolutionary multi-objective optimization for meshsimplification of 3D open models”, Integrated Computer-Aided Engineering, Vol. 20, No. 4, pp. 375–390, 2013.

39. Oliver Schutze,Victor Adrian Sosa Hernandez, Heike Trautmann and Gunter Rudolph, “The hypervolume based directedsearch method for multi-objective optimization problems”, Journal of Heuristics, Vol. 22, No. 3, pp. 273–300, June2016.

40. J.M. Luna, J.R. Romero and S. Ventura, “Grammar-based multi-objective algorithms for mining association rules”, Data& Knowledge Engineering, Vol. 86, pp. 19–37, July 2013.

41. Ana Respicio, Margarida Moz and Margarida Vaz Pato, “Enhanced genetic algorithms for a bi-objective bus driverrostering problem: a computational study”, International Transactions in Operational Research, Vol. 20, No. 4, pp.443–470, July 2013.

42. Rodolfo Eleazar Perez Loaiza, Elias Olivares-Benitez, Pablo A. Miranda Gonzalez, Aaron Guerrero Campanur and JoseLuis Martinez Flores, “Supply chain network design with efficiency, location, and inventory policy using a multiobjectiveevolutionary algorithm”, International Transactions in Operational Research, Vol. 24, Nos. 1-2, pp. 251–275, January-March 2017.

43. Sergio Nesmachnow, Cristian Perfumo and Inigo Goiri, “Holistic multiobjective planning of datacenters powered byrenewable energy”, Cluster Computing–Journal of Networks Software Tools and Applications, Vol. 18, No. 4, pp.1379–1397, December 2015.

44. Santiago Iturriaga, Bernabe Dorronsoro and Sergio Nesmachnow, “Multiobjective evolutionary algorithms for energy andservice level scheduling in a federation of distributed datacenters”, International Transactions in Operational Research,Vol. 24, Nos. 1-2, pp. 199–228, January-March 2017.

45. Alberto Fernandez, Victoria Lopez, Maria Jose del Jesus and Francisco Herrera, “Revisiting Evolutionary Fuzzy Systems:Taxonomy, applications, new trends and challenges”, Knowledge-Based Systems, Vol. 80, pp. 109–121, May 2015.

46. Gift Dumedah, “Toward essential union between evolutionary strategy and data assimilation for model diagnostics:An application for reducing the search space of optimization problems using hydrologic genome map”, EnvironmentalModelling & Software, Vol. 69, pp. 342–352, July 2015.

47. Amir-Hasan Kakaee, Pourya Rahnama, Amin Paykani and Behrooz Mashadi, “Combining artificial neural network andmulti-objective optimization to reduce a heavy-duty diesel engine emissions and fuel consumption”, Journal of CentralSouth University, Vol. 22, No. 11, pp. 4235–4245, November 2015.

48. Shana Schlottfeldt, Maria Emilia M.T. Walter, Andre Carlos P.L.F. de Carvalho, Thannya N. Soares, Mariana P.C.Telles, Rafael D. Loyola and Jose Alexandre F. Diniz, “Multi-objective optimization for plant germplasm collectionconservation of genetic resources based on molecular variability”, Tree Genetics & Genomes, Vol. 11, No. 2, ArticleNumber: 16, April 2015.

49. Joshua T. Knight, David J. Singer and Matthew D. Collette, “Testing of a spreading mechanism to promote diversity inmulti-objective particle swarm optimization”, Optimization and Engineering, Vol. 16, No. 2, pp. 279–302, June 2015.

50. Abolfazl Khalkhali, Majid Mostafapour, Seyed Mohamad Tabatabaie and Behnam Ansari, “Multi-objective crashwor-thiness optimization of perforated square tubes using modified NSGAII and MOPSO”, Structural and MultidisciplinaryOptimization, Vol. 54, No. 1, pp. 45–61, July 2016.

51. Hiroyuki Sato, “Analysis of inverted PBI and comparison with other scalarizing functions in decomposition basedMOEAs”, Journal of Heuristics, Vol. 21, No. 6, pp. 819–849, December 2015.

52. Minami Miyakawa, Keiki Takadama and Hiroyuki Sato, “Controlling selection areas of useful infeasible solutions fordirected mating in evolutionary constrained multi-objective optimization”, Annals of Mathematics and Artificial Intel-ligence, Vol. 76, Nos. 1-2, pp. 25–46, February 2016.

53. Oliver Schutze, Christian Dominguez-Medina, Nareli Cruz-Cortes, Luis Gerardo de la Fraga, Jian-Qiao Sun, GregorioToscano, Ricardo Landa, “A scalar optimization approach for averaged Hausdorff approximations of the Pareto front”,Engineering Optimization, Vol. 48, No. 9, pp. 1593–1617, 2016.

54. David Hadka and Patrick Reed, “Large-scale parallelization of the Borg multiobjective evolutionary algorithm to enhancethe management of complex environmental systems”, Environmental Modelling & Software, Vol. 69, pp. 353–369, July2015.

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55. Feifei Zheng, Aaron C. Zecchin, Holger R. Maier and Angus R. Simpson, “Comparison of the Searching Behavior ofNSGA-II, SAMODE, and Borg MOEAs Applied to Water Distribution System Design Problems”, Journal of WaterResources Planning and Management, Vol. 142, No. 7, Article Number: 04016017, July 2016.

56. Iraklis-Dimitrios Psychas, Eleni Delimpasi and Yannis Marinakis, “Hybrid evolutionary algorithms for the MultiobjectiveTraveling Salesman Problem”, Expert Systems with Applications, Vol. 42, No. 22, pp. 8956–8970, December 1, 2015.

57. Cai Dai and Yiping Wang, “A new uniform evolutionary algorithm based on decomposition and CDAS for many-objectiveoptimization”, Knowledge-Based Systems, Vol. 85, pp. 131–142, September 2015.

58. David Hadka, Jonathan Herman, Patrick Reed and Klaus Keller, “An open source framework for many-objective robustdecision making”, Environmental Modelling & Software, Vol. 74, pp. 114–129, December 2015.

59. Evgenii S. Matrosov, Ivana Huskova, Joseph R. Kasprzyk, Julien J. Harou, Chris Lambert and Patrick M. Reed, “Many-objective optimization and visual analytics reveal key trade-offs for London’s water supply”, Journal of Hydrology, Vol.531, pp. 1040–1053, December 2015.

60. Yicha Zhang, Weijun Wang and Alain Bernard, “Embedding Multi-Attribute Decision Making into Evolutionary Opti-mization to Solve the Many-Objective Combinatorial Optimization Problems”, Journal of Grey System, Vol. 28, No. 3,pp. 124–143, 2016.

61. Rebecca Smith, Joseph Kasprzyk and Edith Zagona, “Many-Objective Analysis to Optimize Pumping and Releases inMultireservoir Water Supply Network”, Journal of Water Resources Planning and Management, Vol. 142, No. 2, ArticleNumber: 04015049, February 2016.

62. Haitham Seada and Kalyanmoy Deb, “A Unified Evolutionary Optimization Procedure for Single, Multiple, and ManyObjectives”, IEEE Transactions on Evolutionary Computation, Vol. 20, No. 3, pp. 358–369, June 2016.

63. E.B. Schlunz, P.M. Bokov and J.H. van Vuuren, “A comparative study on multiobjective metaheuristics for solvingconstrained in-core fuel management optimisation problems”, Computers & Operations Research, Vol. 75, pp. 174–190,November 2016.

64. Mehran Shaygan, Abbas Alimohammadi, Ali Mansourian, Zohreh Shams Govara and S. Mostapha Kalami, “SpatialMulti-Objective Optimization Approach for Land Use Allocation Using NSGA-II”, IEEE Journal of Selected Topics inApplied Earth Observations and Remote Sensing, Vol. 7, No. 3, pp. 906–916, March 2014.

65. Gunter Rudolph, Oliver Schutze, Christian Grimme, Christian Dominguez-Medina and Heike Trautmann, “Optimalaveraged Hausdorff archives for bi-objective problems: theoretical and numerical results”, Computational Optimizationand Applications, Vol. 64, No. 2, pp. 589–618, June 2016.

66. Seyedali Mirjalili, Shahrzad Saremi, Seyed Mohammad Mirjalili and Leadro dos S. Coelho, “Multi-objective grey wolfoptimizer: A novel algorithm for multi-criterion optimization”, Expert Systems with Applications, Vol. 47, pp. 106–119,April 1, 2016.

67. Xianpeng Wang and Lixin Tang, “An adaptive multi-population differential evolution algorithm for continuous multi-objective optimization”, Information Sciences, Vol. 348, pp. 124–141, June 20, 2016.

68. M.V.C. da Silva, N. Nedjah and L.M. Mourelle, “Power-aware multi-objective evolutionary optimisation for applicationmapping on network-on-chip platforms”, International Journal of Electronics, Vol. 97, pp. 1163–1179, 2010.

69. Victor Berrocal-Plaza, Miguel A. Vega-Rodriguez and Juan M. Sanchez-Perez, “On the use of multiobjective optimizationfor solving the Location Areas strategy with different paging procedures in a realistic mobile network”, Applied SoftComputing, Vol. 18, pp. 146–157, May 2014.

70. Muhammad Burhan, Kian Jon Ernest Chua and Kim Choon Ng, “Sunlight to hydrogen conversion: Design optimizationand energy management of concentrated photovoltaic (CPV-Hydrogen) system using micro genetic algorithm”, Energy,Vol. 99, pp. 115–128, March 15, 2016.

71. B.J. Hancock, T.B. Nysetvold and C.A. Mattson, “L-dominance: An approximate-domination mechanism for adaptiveresolution of Pareto frontiers”, Structural and Multidisciplinary Optimization, Vol. 52, No. 2, pp. 269–279, August 2015.

72. Kunjie Yu, Xin Wang and Zhenlei Wang, “Self-adaptive multi-objective teaching-learning-based optimization and itsapplication in ethylene cracking furnace operation optimization”, Chemometrics and Intelligent Laboratory Systems, Vol.146, pp. 198–210, August 15, 2015.

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74. Wali Khan Mashwani,Abdellah Salhi, Muhammad Asif Jan, Muhammad Sulaiman, Rashida Adeeb Khanum and Abdul-mohsen Algarni, “Evolutionary Algorithms Based on Decomposition and Indicator Functions: State-of-the-art Survey”,International Journal of Advanced Computer Science and Applications, Vol. 7, No. 2, pp. 583–593, February 2016.

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76. Alvaro Garcia-Piquer, Andreu Sancho-Asensio, Albert Fornells, Elisabet Golobardes, Guiomar Corral and FrancescTeixido-Navarro, “Toward high performance solution retrieval in multiobjective clustering”, Information Sciences, Vol.320, pp. 12–25, November 1, 2015.

77. Maoguo Gong, Mingyang Zhang and Yuan Yuan, “Unsupervised Band Selection Based on Evolutionary MultiobjectiveOptimization for Hyperspectral Images”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 54, No. 1, pp.544–557, January 2016.

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79. Chenwen Zhu, Lihong Xu and Erik D. Goodman, “Generalization of Pareto-Optimality for Many-Objective EvolutionaryOptimization”, IEEE Transactions on Evolutionary Computation, Vol. 20, No. 2, pp. 299–315, April 2016.

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81. Paolo Campigotto, Andrea Passerini and Roberto Battiti, “Active Learning of Pareto Fronts”, IEEE Transactions onNeural Networks and Learning Systems, Vol. 25, No. 3, pp. 506–519, March 2014.

82. Weiyang Tong, Souma Chowdhury and Achille Messac, “A multi-objective mixed-discrete particle swarm optimizationwith multi-domain diversity preservation”, Structural and Multidisciplinary Optimization, Vol. 53, No. 3, pp. 471–488,March 2016.

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90. Liangjun Ke, Qingfu Zhang and Roberto Battiti, “MOEA/D-ACO: A Multiobjective Evolutionary Algorithm UsingDecomposition and Ant Colony”, IEEE Transactions on Cybernetics, Vol. 43, No. 6, pp. 1845–1859, December 2013.

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102. Patrick M. Reed and Joshua B. Kollat, “Visual analytics clarify the scalability and effectiveness of massively parallelmany-objective optimization: A groundwater monitoring design example”, Advances in Water Resources, Vol. 56, pp.1–13, June 2013.

103. Ioannis Tsoukalas and Christos Makropoulos, “Multiobjective optimisation on a budget: Exploring surrogate modellingfor robust multi-reservoir rules generation under hydrological uncertainty”, Environmental Modelling & Software, Vol.69, pp. 396–413, July 2015.

104. S. Datta and P.P Chattopadhyay, “Soft computing techniques in advancement of structural metals”, InternationalMaterials Review, Vol. 58, No. 8, pp. 475–504, November 2013.

105. Krishnaswamy Hariharan, Ngoc-Trung Nguyen, Nirupam Chakraborti, Myoung-Gyu Lee and Frederic Barlat, “Multi-Objective Genetic Algorithm to Optimize Variable Drawbead Geometry for Tailor Welded Blanks Made of DissimilarSteels”, Steel Research International, Vol. 85, No. 12, pp. 1597–1607, December 2014.

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107. Ke Li, Kalyanmoy Deb, Qingfu Zhang and Sam Kwong, “An Evolutionary Many-Objective Optimization AlgorithmBased on Dominance and Decomposition”, IEEE Transactions on Evolutionary Computation, Vol. 19, No. 5, pp.694–716, October 2015.

108. Karl Bringmann, Tobias Friedrich, Christian Igel and Thomas Voss, “Speeding up many-objective optimization by MonteCarlo approximations”, Artificial Intelligence, Vol. 204, pp. 22–29, November 2013.

109. Sen Bong Gee, Kay Chen Tan, Vui Ann Shim and Nikhil R. Pal, “Online Diversity Assessment in Evolutionary Multi-objective Optimization: A Geometrical Perspective”, IEEE Transactions on Evolutionary Computation, Vol. 19, No. 4,pp. 542–559, August 2015.

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112. Yi Xiang and Yuren Zhou, “A dynamic multi-colony artificial bee colony algorithm for multi-objective optimization”,Applied Soft Computing, Vol. 35, pp. 766–785, October 2015.

113. Hossein Karshenas, Concha Bielza and Pedro Larranaga, “Interval-based ranking in noisy evolutionary multi-objectiveoptimization”, Computational Optimization and Applications, Vol. 61, No. 2, pp. 517–555, June 2015.

114. Rajan Filomeno Coelho, “Probabilistic Dominance in Multiobjective Reliability-Based Optimization: Theory and Im-plementation”, IEEE Transactions on Evolutionary Computation, Vol. 19, No. 2, pp. 214–224, April 2015.

115. Carolina Lagos, Broderick Crawford, Enrique Cabrera, Ricardo Soto, Jose-Miguel Rubio and Fernando Paredes, “Com-paring Evolutionary Strategies on a Biobjective Cultural Algorithm”, Scientific World Journal, Article Number: 745921,2014.

116. Nyambayar Baatar, Minh-Trien Pham and Chang-Seop Koh, “Multiguiders and Nondominate Ranking DifferentialEvolution Algorithm for Multiobjective Global Optimization of Electromagnetic Problems”, IEEE Transactions onMagnetics, Vol. 49, No. 5, pp. 2105–2108, May 2013.

117. Ankur Sinha, Pekka Korhonen, Jyrki Wallenius and Kalyanmoy Deb, “An interactive evolutionary multi-objectiveoptimization algorithm with a limited number of decision maker calls”, European Journal of Operational Research, Vol.233, No. 3, pp. 674–688, March 16, 2014.

118. Ahmad Mozaffari, Mofid Gorji-Bandpy, Pendar Samadian, Rouzbeh Rastgar and Alireza Rezania Kolaei, “Compre-hensive preference optimization of an irreversible thermal engine using pareto based mutable smart bee algorithm andgeneralized regression neural network”, Swarm and Evolutionary Computation, Vol. 9, pp. 90–103, April 2013.

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119. R.C. Gutierrez-Urquidez, G. Valencia-Palomo, O.M. Rodriguez-Elias and L. Trujillo, “Systematic selection of tuningparameters for efficient predictive controllers using a multiobjective evolutionary algorithm”, Applied Soft Computing,Vol. 31, pp. 326–338, June 2015.

120. Daniele Prada, Marco Bellini, Ivica Stevanovic, Laurent Lemaitre, James Victory, Jan Vobecky, Riccardo Sacco and PeterO. Lauritzen, “On the Performance of Multiobjective Evolutionary Algorithms in Automatic Parameter Extraction ofPower Diodes”, IEEE Transactions on Power Electronics, Vol. 30, No. 9, pp. 4986–4997, September 2015.

121. Javier Rubio-Loyola, Gregorio Toscano-Pulido, Marinos Charalambides, Marisol Magana-Aguilar, Joan Serrat-Fernandez,George Pavlou and Hiram Galeana-Zapien, “Business-driven policy optimization for service management”, InternationalJournal of Network Management, Vol. 25, No. 2, pp. 113–140, March-April 2015.

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123. Ana Belen Ruiz, Ruben Saborido and Mariano Luque, “A preference-based evolutionary algorithm for multiobjectiveoptimization: the weighting achievement scalarizing function genetic algorithm”, Journal of Global Optimization, Vol.62, No. 1, pp. 101–129, May 2015.

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125. Xiaoguang He, Cai Dai and Zehua Chen, “Many-Objective Optimization Using Adaptive Differential Evolution with aNew Ranking Method”, Mathematical Problems in Engineering, Article Number: 259473, 2014.

126. Cai Dai, Yuping Wang and Miao Ye, “A new evolutionary algorithm based on contraction method for many-objectiveoptimization problems”, Applied Mathematics and Computation, Vol. 245, pp. 191–205, October 15, 2014.

127. A. Kaveh and K. Laknejadi, “A new multi-swarm multi-objective optimization method for structural design”, Advancesin Engineering Software, Vol. 58, pp. 54–69, April 2013.

128. Sanghamitra Bandyopadhyay, Rudrasis Chakraborty and Ujjwal Maulik, “Priority based epsilon dominance: A newmeasure in multiobjective optimization”, Information Sciences, Vol. 305, pp. 97–109, June 1, 2015.

129. Ernestas Filatovas, Olga Kurasova and Karthik Sendhya, “Synchronous R-NSGA-II: An Extended Preference-BasedEvolutionary Algorithm for Multi-Objective Optimization”, Informatica, Vol. 26, No. 1, pp. 33–50, 2015.

130. Proteek Chandan Roy, Md. Monirul Islam, Kazuyuki Murase and Xin Yao, “Evolutionary Path Control Strategy forSolving Many-Objective Optimization Problem”, IEEE Transactions on Cybernetics, Vol. 45, No. 4, pp. 702–715, April2015.

131. Bili Chen, Wenhua Zeng, Yangbin Lin and Defu Zhang, “A New Local Search-Based Multiobjective OptimizationAlgorithm”, IEEE Transactions on Evolutionary Computation, Vol. 19, No. 1, pp. 50–73, February 2015.

132. Hossein Rajabalipour Cheshmehgaz, Mohammad Ishak Desa and Antoni Wibowo, “Effective local evolutionary searchesdistributed on an island model solving bi-objective optimization problems”, Applied Intelligence, Vol. 38, No. 3, pp.331–356, April 2013.

133. Bernabe Dorronsoro, Gregoire Danoy, Antonio J. Nebro and Pascal Bouvry, “Achieving super-linear performance in par-allel multi-objective evolutionary algorithms by means of cooperative coevolution”, Computers & Operations Research,Vol. 40, No. 6, pp. 1552–1563, June 2013.

134. Mohammad Mortazavi-Naeini, George Kuczera and Lijie Cui, “Efficient multi-objective optimization methods for com-putationally intensive urban water resources models”, Journal of Hydroinformatics, Vol. 17, No. 1, pp. 36–55, 2015.

135. Jurgen Branke, Salvatore Greco, Roman Slowinski and Piotr Zielniewicz, “Learning Value Functions in InteractiveEvolutionary Multiobjective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 19, No. 1, pp.88–102, February 2015.

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137. Matthew P. Ferringer, Ronald S. Clifton and Timothy G. Thompson, “Efficient and accurate evolutionary multi-objectiveoptimization paradigms for satellite constellation design”, Journal of Spacecraft and Rockets, Vol. 44, No. 3, pp. 682–691,May-June 2007.

138. Han-Young Park, Akhil Datta-Gupta and Michael J. King, “Handling conflicting multiple objectives using Pareto-basedevolutionary algorithm during history matching of reservoir performance”, Journal of Petroleum Science and Engineering,Vol. 125, pp. 48–66, January 2015.

139. Jianhua Xiao, Jin Xu, Xiutang Geng and Linqiang Pan, “Multi-objective carrier chaotic evolutionary algorithm for DNAsequences design”, Progress in Natural Science, Vol. 17, No. 12, pp. 1515–1520, December 2007.

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140. Juan Rada-Vilela, Manuel Chica, Oscar Cordon and Sergio Damas, “A comparative study of Multi-Objective Ant ColonyOptimization algorithms for the Time and Space Assembly Line Balancing Problem”, Applied Soft Computing, Vol. 13,No. 11, pp. 4370–4382, November 2013.

141. Jinn-Tsong Tsai, Ching-I. Yang and Jyh-Horng Chou, “Hybrid sliding level Taguchi-based particle swarm optimizationfor flowshop scheduling problems”, Applied Soft Computing, Vol. 15, pp. 177–192, February 2014.

142. Hossein Rajabalipour Cheshmehgaz, Habibollah Haron and Abdollah Sharifi, “The review of multiple evolutionarysearches and multi-objective evolutionary algorithms”, Artificial Intelligence Review, Vol. 43, No. 3, pp. 311–343,March 2015.

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146. Miguel Porto, Otilia Correia and Pedro Beja, “Optimization of Landscape Services under Uncoordinated Managementby Multiple Landowners”, Plos One, Vol. 9, No. 1, Article Number: e86001, January 17, 2014.

147. Xue-Song Yang, Bing-Zhong Wang, Sai Ho Yeung, Quan Xue and Kim Fung Man, “Circularly Polarized ReconfigurableCrossed-Vagi Patch Antenna”, IEEE Antennas and Propagation Magazine, Vol. 53, No. 5, pp. 65–80, October 2011.

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149. Wesley Klewerton Guez Assuncao, Thelma Elita Colanzi, Silvia Regina Vergilio and Aurora Pozo, “A multi-objectiveoptimization approach for the integration and test order problem”, Information Sciences, Vol. 267, pp. 119–139, May20, 2014.

150. Krishnaswamy Hariharan, Nirupam Chakraborti, Frederic Barlat and Myoung-Gyu Lee, “A Novel Multi-objective Ge-netic Algorithms-Based Calculation of Hill’s Coefficients”, Metallurgical and Materials Transactions A–Physical Metal-lurgy and Materials Science, Vol. 45A, No. 6, pp. 2704–2707, June 2014.

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152. Khairy Elsayed and Chris Lacor, “ Robust parameter design optimization using Kriging, RBF and RBFNN with gradient-based and evolutionary optimization techniques”, Applied Mathematics and Computation, Vol. 236, pp. 325–344, June1, 2014.

153. Lianbo Ma, Kunyuan Hu, Yunlong Zhu and Hanning Chen, “Cooperative artificial bee colony algorithm for multi-objective RFID network planning”, Journal of Network and Computer Applications, Vol. 42, pp. 143–162, June 2014.

154. Ehsan Gholamalizadeh and Man-Hoe Kim, “Thermo-economic triple-objective optimization of a solar chimney powerplant using genetic algorithms”, Energy, Vol. 70, pp. 204–211, June 1, 2014.

155. Miqing Li, Shengxiang Yang and Xiaohui Liu, “Diversity Comparison of Pareto Front Approximations in Many-ObjectiveOptimization”, IEEE Transactions on Cybernetics, Vol. 44, No. 12, pp. 2568–2584, December 2014.

156. Huseyin Onur Mete and Zelda B. Zabinsky, “Multiobjective Interacting Particle Algorithm for Global Optimization”,INFORMS Journal on Computing, Vol. 26, No. 3, pp. 500–513, Summer 2014.

157. Kostas Florios and George Mavrotas, “Generation of the exact Pareto set in Multi-Objective Traveling Salesman andSet Covering Problems”, Applied Mathematics and Computation, Vol. 237, pp. 1–19, June 15, 2014.

158. Enze Zhang, Yifei Wu and Qingwei Chen, “ A practical approach for solving multi-objective reliability redundancyallocation problems using extended bare-bones particle swarm optimization”, Reliability Engineering & System Safety,Vol. 127, pp. 65–76, July 2014.

159. S. Sinaie, A. Heidarpour and X.L. Zhao, “A multi-objective optimization approach to the parameter determination ofconstitutive plasticity models for the simulation of multi-phase load histories”, Computers & Structures, Vol. 138, pp.112–132, July 1, 2014.

160. Jose D. Martinez-Morales, Elvia R. Palacios-Hernandez and Gerardo A. Velazquez-Carrillo, “Artificial neural networkbased on genetic algorithm for emissions prediction of a SI gasoline engine”, Journal of Mechanical Science and Tech-nology, Vol. 28, No. 6, pp. 2417–2427, June 2014.

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163. You-Jin Park, Rong Pan, Connie M. Borror, Douglas C. Montgomery and Gyu-Bong Lee, “Simultaneous Improvementof Energy Efficiency and Product Quality in PCB Lamination Process”, International Journal of Precision Engineeringand Manufacturing–Green Technology, Vol. 1, No. 3, pp. 247–256, July 2014.

164. Weijian Kong, Tianyou Chai, Jinliang Ding and Shengxiang Yang, “Multifurnace Optimization in Electric SmeltingPlants by Load Scheduling and Control”, IEEE Transactions on Automation Science and Engineering, Vol. 11, No. 3,pp. 850–862, July 2014.

165. Kamal Boudjelaba, Frederic Ros and Djamel Chikouche, “Adaptive genetic algorithm-based approach to improve thesynthesis of two-dimensional finite impulse response filters”, IET Signal Processing, Vol. 8, No. 5, pp. 429–446, July2014.

166. Michele Amoretti, “Evolutionary strategies for ultra-large-scale autonomic systems”, Information Sciences, Vol. 274,pp. 1–16, August 1, 2014.

167. Julien Schleich, Gregoire Danoy, Bernabe Dorronsoro and Pascal Bouvry, “Optimising small-world properties in VANETs:Centralised and distributed overlay approaches”, Applied Soft Computing, Vol. 21, pp. 637–646, August 2014.

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169. Masoud Sharafi and Tarek Y. ELMekkawy, “Multi-objective optimal design of hybrid renewable energy systems usingPSO-simulation based approach”, Renewable Energy, Vol. 68, pp. 67–79, August 2014.

170. Mehmet Unal and Gordon P. Warn, “Optimal cost-effective topology of column bearings for reducing vertical accelerationdemands in multistory base-isolated buildings”, Earthquake Engineering & Structural Dynamics, Vol. 43, No. 8, pp.1107–1127, July 10, 2014.

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184. Manojkumar Ramteke and Santosh K. Gupta, “Biomimetic Adaptations of GA and SA for the Robust MO Optimizationof an Industrial Nylon-6 Reactor”, Materials and Manufacturing Processes, Vol. 24, No. 1, pp. 38–46, Article Number:PII 906599196, 2009.

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189. Francesco Folino and Clara Pizzuti, “An Evolutionary Multiobjective Approach for Community Discovery in DynamicNetworks”, IEEE Transactions on Knowledge and Data Engineering, Vol. 26, No. 8, pp. 1838–1852, August 2014.

190. Joseph R. Kasprzyk, Patrick M. Reed, Gregory W. Characklis and Brian R. Kirsch, “Many-objective de Novo watersupply portfolio planning under deep uncertainty”, Environmental Modelling & Software, Vol. 34, pp. 87–104, June2012.

191. Joseph R. Kasprzyk, Shanti Nataraj, Patrick M. Reed and Robert J. Lempert, “Many objective robust decision makingfor complex environmental systems undergoing change”, Environmental Modelling & Software, Vol. 42, pp. 55–71, April2013.

192. Hideki Katagiri, Ichiro Nishizaki, Tomohiro Hayashida and Takanori Kadoma, “Multiobjective Evolutionary Optimiza-tion of Training and Topology of Recurrent Neural Networks for Time-Series Prediction”, Computer Journal, Vol. 55,No. 3, pp. 325–336, March 2012.

193. Kamyab Tahernezhad, Bazargan Lari, Ali Hamzeh and Sattar Hashemi, “HC-MOEA: A hierarchical clustering approachfor increasing the solution’s diversity in multiobjective evolutionary algorithms”, Intelligent Data Analysis, Vol. 19, No.1, pp. 187–208, 2015.

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195. Mariano Frutos and Fernando Tohme, “Evolutionary Multi-Objective Scheduling Procedures in Non-Standardized Pro-duction Processes”, DYNA-Colombia, Vol. 79, No. 172, pp. 101–107, April 2012.

196. Masatoshi Sakawa, Hideki Katagiri and Takeshi Matsui, “Interactive fuzzy stochastic two-level integer programmingthrough fractile criterion optimization”, Operational Research, Vol. 12, No. 2, pp. 209–227, August 2012.

197. Saeb M. Besarati and D. Yogi Goswami, “A computationally efficient method for the design of the heliostat field forsolar power tower plant”, Renewable Energy, Vol. 69, pp. 226–232, September 2014.

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199. Singiresu S. Rao, Hoe-Gil Lee and Yi Hu, “Optimal Design of Compound Parabolic Concentrator Solar Collector System”,Journal of Mechanical Design, Vol. 136, No. 9, Article Number: 091402, September 2014.

200. Mojtaba Shivaie, Ahmad Salemnia and Mohammad T. Ameli, “A multi-objective approach to optimal placement andsizing of multiple active power filters using a music-inspired algorithm”, Applied Soft Computing, Vol. 22, pp. 189–204,September 2014.

201. I. Kaliszewski and J. Miroforidis, “Two-Sided Pareto Front Approximations”, Journal of Optimization Theory andApplications, Vol. 162, No. 3, pp. 845–855, September 2014.

202. M.J. Gacto, M. Galende, R. Alcala and F. Herrera, “METSK-HDe: A multiobjective evolutionary algorithm to learnaccurate TSK-fuzzy systems in high-dimensional and large-scale regression problems”, Information Sciences, Vol. 276,pp. 63–79, August 20, 2014.

203. Claudio Comis Da Ronco, Rita Ponza and Ernesto Benini, “Aerodynamic Shape Optimization in Aeronautics: A Fastand Effective Multi-Objective Approach”, Archives of Computational Methods in Engineering, Vol. 21, No. 3, pp.189–271, September 2014.

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206. Tian Liang, Wei Heng, Chao Meng and Guodong Zhang, “Cooperative Power Allocation Based on Multi-ObjectiveIntelligent Optimization for Multi-Source Multi-Relay Networks”, IEICE Transactions on Communications, Vol. E97B,pp. 1938–1946, September 2014.

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208. Nirupam Chakraborti, “Critical Assessment 3: The unique contributions of multi-objective evolutionary and geneticalgorithms in materials research”, Materials Science and Technology, Vol. 30, No. 11, pp. 1259–1262, September 2014.

209. Giacomo F. Porzio, Valentina Colla, Nicola Matarese, Gianluca Nastasi, Teresa A. Branca, Alessandro Amato, BarbaraFornai, Marco Vannucci and Massimo Bergamasco, “Process integration in energy and carbon intensive industries: Anexample of exploitation of optimization techniques and decision support”, Applied thermal Engineering, Vol. 70, No. 2,pp. 1148–1155, September 22, 2014.

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211. Yashar Hashemi and Khalil Valipour, “FDM based multi-objective optimal sitting and design of TC-FLSFCL for studyof distribution system reliability”, International Journal of Electrical Power & Energy Systems, Vol. 61, pp. 463–473,October 2014.

212. P. Sharafi, Lip H. Teh and Muhammad N.S. Hadi, “Shape optimization of thin-walled steel sections using graph theoryand ACO algorithm”, Journal of Constructional Steel Research, Vol. 101, pp. 331–341, October 2014.

213. M. Frutos, M. Mendez, F. Tohme and D. Broz, “Comparison of Multiobjective Evolutionary Algorithms for OperationsScheduling under Machine Availability Constraints”, Scientific World Journal, Article Number: 418396, 2013.

214. Steve O’Hagan, Joshua Knowles and Douglas B. Kell, “Exploiting Genomic Knowledge in Optimising Molecular BreedingProgrammes: Algorithms from Evolutionary Computing”, Plos One, Vol. 7, No. 11, Article Number: e48862, November21, 2012.

215. Y. Zhang, A.L. Collins and R.D. Gooday, “Application of the FARMSCOPER tool for assessing agricultural diffusepollution mitigation methods across the Hampshire Avon Demonstration Test Catchment, UK”, Environmental Science& Policy, Vol. 24, pp. 120–131, December 2012.

216. Eduardo Fernandez and Gonzalo Besuievsky, “Inverse lighting design for interior buildings integrating natural andartificial sources”, Computers & Graphics-UK, Vol. 36, No. 8, pp. 1096–1108, December 2012.

217. Hilary E. Brown, Siddharth Suryanarayanan, Sudarshan A. Natarajan and Sanjay Rajopadhye, “Improving Reliabilityof Islanded Distribution Systems With Distributed Renewable Energy Resources”, IEEE Transactions on Smart Grid,Vol. 3, No. 4, pp. 2028–2038, December 2012.

218. Nicolas Jozefowiez, Gilbert Laporte and Frederic Semet, “A Generic Branch-and-Cut Algorithm for MultiobjectiveOptimization Problems: Application to the Multilabel Traveling Salesman Problem”, INFORMS Journal on Computing,Vol. 24, No. 4, pp. 554–564, Fall 2012.

219. Andrea Cynthia Santos, Diego Rocha Lima and Dario Jose Aloise, “Modeling and solving the bi-objective minimumdiameter-cost spanning tree problem”, Journal of Global Optimization, Vol. 60, No. 2, pp. 195–216, October 2014.

220. Gustavo R. Zavala, Antonio J. Nebro, Juan J. Durillo and Francisco Luna, “Integrating a multi-objective optimizationframework into a structural design software”, Advances in Engineering Software, Vol. 76, pp. 161–170, October 2014.

221. Victor Berrocal-Plaza, Miguel A. Vega-Rodriguez and Juan M. Sanchez-Perez, “Solving the location areas managementproblem with multi-objective evolutionary strategies”, Wireless Networks, Vol. 20, No. 7, pp. 1909–1924, October 2014.

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224. Danilo Sipoli Sanches, Joao Bosco A. London, Jr., Alexandre Claudio B. Delbem, Ricardo S. Prado, Federico G.Guimaraes, Oriane M. Neto and Telma W. de Lima, “Multiobjective evolutionary algorithm with a discrete differ-ential mutation operator developed for service restoration in distribution systems”, International Journal of ElectricalPower & Energy Systems, Vol. 62, pp. 700–711, November 2014.

225. Francisco G. Montoya, Francisco Manzano-Agugliaro, Sergio Lopez-Marquez, Quetzalcoatl Hernandez-Escobedo andConsolacion Gil, “Wind turbine selection for wind farm layout using multi-objective evolutionary algorithms”, ExpertSystems with Applications, Vol. 41, No. 15, pp. 6585–6595, November 1, 2014.

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226. Hanieh Borhanazad, Saad Mekhilef, Velappa Gounder Ganapathy, Mostafa Modiri-Delshad and Ali Mirtaheri, “Opti-mization of micro-grid system using MOPSO”, Renewable Energy, Vol. 71, pp. 295–306, November 2014.

227. Michela Fazzolari, Rafael Alcala and Francisco Herrera, “A multi-objective evolutionary method for learning granularitiesbased on fuzzy discretization to improve the accuracy-complexity trade-off of fuzzy rule-based classification systems: D-MOFARC algorithm”, Applied Soft Computing, Vol. 24, pp. 470–481, November 2014.

228. Wassim Ayadi and Jin-Kao Hao, “A memetic algorithm for discovering negative correlation biclusters of DNA microarraydata”, Neurocomputing, Vol. 145, pp. 14–22, December 5, 2014.

229. Bilel Derbel, Jeremie Humeauc, Arnaud Liefooghe and Sebastien Verel, “Distributed localized bi-objective search”,European Journal of Operational Research, Vol. 239, No. 3, pp. 731–743, December 16, 2014.

230. Ke Li and Sam Kwong, “A general framework for evolutionary multiobjective optimization via manifold learning”,Neurocomputing, Vol. 146, pp. 65–74, December 25, 2014.

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232. Gilberto Reynoso-Meza, Javier Sanchis, Xavier Blasco and Sergio Garcia-Nieto, “Physical programming for preferencedriven evolutionary multi-objective optimization”, Applied Soft Computing, Vol. 24, pp. 341–362, November 2014.

233. Kenneth Sorensen and Johan Springael, “Progressive Multi-Objective Optimization”, International Journal of Informa-tion Technology & Decision Making, Vol. 13, No. 5, pp. 917–936, September 2014.

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235. M.J. Mahmoodabadi, M. Taherkhorsandi and A. Bagheri, “Pareto Design of State Feedback Tracking Control of aBiped Robot via Multiobjective PSO in Comparison with Sigma Method and Genetic Algorithms: Modified NSGAIIand MATLAB’s Toolbox”, Scientific World Journal, Article Number: 303101, 2014.

236. Christopher Smith and Yaochu Jin, “Evolutionary multi-objective generation of recurrent neural network ensembles fortime series prediction”, Neurocomputing, Vol. 143, pp. 302–311, November 2, 2014.

237. Bernardo Severino, Felipe Gana, Rodrigo Palma-Behnke, Pablo A. Estevez, Williams R. Calderon-Munoz, Marcos E.Orchard, Jorge Reyes and Marcelo Cortes, “Multi-objective optimal design of lithium-ion battery packs based on evolu-tionary algorithms”, Journal of Power Sources, Vol. 267, pp. 288–299, December 1, 2014.

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251. Abdelkader Boukhobza, Abdennacer Bounoua, Abdelmalik Taleb-Ahmed and Nasreddine Taleb, “Design of BiorthogonalFilter Banks Using a Multi-objective Genetic Algorithm for an Image Coding Scheme”, Circuits Systems and SignalProcessing, Vol. 32, No. 4, pp. 1725–1744, August 2013.

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261. Brijesh Kumar Giri, Jussi Hakanen, Kaisa Miettinen and Nirupam Chakraborti, “Genetic programming through bi-objective genetic algorithms with a study of a simulated moving bed process involving multiple objectives”, Applied SoftComputing, Vol. 13, No. 5, pp. 2613–2623, May 2013.

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265. Alvaro Garcia-Piquer, Albert Fornells, Jaume Bacardit, Albert Orriols-Puig and Elisabet Golobardes, “Large-ScaleExperimental Evaluation of Cluster Representations for Multiobjective Evolutionary Clustering”, IEEE Transactions onEvolutionary Computation, Vol. 18, No. 1, pp. 36–53, February 2014.

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270. Yeboon Yun and Hirotaka Nakayama, “Utilizing expected improvement and generalized data envelopment analysis inmulti-objective genetic algorithms”, Journal of Global Optimization, Vol. 57, No. 2, pp. 367–384, October 2013.

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272. Zoran Vujicic, Rogerio P. Dionisio, Ali Shahpari, Natasa B. Pavlovic and Antonio Teixeira, “Efficient Dynamic Modelingof the Reflective Semiconductor Optical Amplifier”, IEEE Journal of Selected Topics in Quantum Electronics, Vol. 19,No. 5, Article Number: 3000310, September-October 2013.

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305. Victor M. Cervantes-Salido, Oswaldo Jaime, Carlos A. Brizuela and Israel M. Martinez-Perez, “Improving the design ofsequences for DNA computing: A multiobjective evolutionary approach”, Applied Soft Computing, Vol. 13, No. 12, pp.4594–4607, December 2013.

306. Aniruddha Basak, Swagatam Das and Kay Chen Tan, “Multimodal Optimization Using a Biobjective Differential Evo-lution Algorithm Enhanced With Mean Distance-Based Selection”, IEEE Transactions on Evolutionary Computation,Vol. 17, No. 5, pp. 666–685, October 2013.

307. Fulya Altiparmak, Mitsuo Gen, Lin Lin and Turan Paksoy, “A genetic algorithm approach for multi-objective optimiza-tion of supply chain networks”, Computers & Industrial Engineering, Vol. 51, pp. 196–215, September 2006.

308. Yu-Jun Zheng, Qing Song and Sheng-Yong Chen, “Multiobjective fireworks optimization for variable-rate fertilizationin oil crop production”, Applied Soft Computing, Vol. 13, No. 11, pp. 4253–4263, November 2013.

309. Karthik Sindhya, Kaisa Miettinen and Kalyanmoy Deb, “A Hybrid Framework for Evolutionary Multi-objective Opti-mization”, IEEE Transactions on Evolutionary Computation, Vol. 17, No. 4, pp. 495–511, August 2013.

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312. Eduardo Fernandez and Rafael Olmedo, “An outranking-based general approach to solving group multi-objective opti-mization problems”, European Journal of Operational Research, Vol. 225, No. 3, pp. 497–506, March 16, 2013.

313. Prakash Shelokar, Arnaud Quirin and Oscar Cordon, “MOSubdue: a Pareto dominance-based multiobjective Subduealgorithm for frequent subgraph mining”, Knowledge and Information Systems, Vol. 34, No. 1, pp. 75–108, January2013.

314. Melissa Gardenghi, Margaret M. Wiecek and Wenshan Wang, “Biobjective optimization for analytical target cascading:optimality vs. achievability”, Structural and Multidisciplinary Optimization, Vol. 47, No. 1, pp. 111–133, January 2013.

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316. Yau-Zen Chang, Kao-Ting Hung, Hsin-Yi Shih and Zhi-Ren Tsai, “Surrogate Neural Network and Multi-Objective DirectAlgorithm for the Optimization of a Swiss-Roll Type Recuperator”, International Journal of Innovative ComputingInformation and Control, Vol. 8, No. 12, pp. 8199–8214, December 2012.

317. Irwanda Laory, Nizar Bel Hadj Ali, Thanh N. Trinh and Ian F.C. Smith, “Measurement System Configuration forDamage Identification of Continuously Monitored Structures”, Journal of Bridge Engineering, Vol. 17, No. 6, pp.857–866, November-December 2012.

318. P. Shahnazari-Shahrezaei, R. Tavakkoli-Moghaddam, M. Azarkish and A. Sadeghnejad-Barkousaraie, “A DifferentialEvolution Algorithm Developed for a Nurse Scheduling Problem”, South African Journal of Industrial Engineering, Vol.23, No. 3, pp. 68–90, November 2012.

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320. Juan Gabriel Correa Medina, Loecelia Guadalupe Ruvalcaba Sanchez, Elias Olivares-Benitez and Vittorio Zanella Pala-cios, “Biobjective Model for Redesigning Sales Territories”, International Journal of Industrial Engineering–Theory,Applications and Practice, Vol. 19, No. 9, pp. 350–358, 2012.

321. Thomas A. Wettergren and Russell Costa, “Optimal Multiobjective Placement of Distributed Sensors against MovingTargets”, ACM Transactions on Sensor Networks, Vol. 8, No. 3, Article Number: 21, 2012.

322. Maik Ringkamp, Sina Ober-Blobaum, Michael Dellnitz and Oliver Schutze, “Handling high-dimensional problems withmulti-objective continuation methods via successive approximation of the tangent space”, Engineering Optimization,Vol. 44, No. 9, pp. 1117–1146, 2012.

323. R. Venkata Rao and V.D. Kalyankar, “Parameter Optimization of Machining Processes Using a New OptimizationAlgorithm”, Materials and Manufacturing Processes, Vol. 27, No. 9, pp. 978–985, 2012.

324. Seyed Reza Hosseini, Majid Amidpour and Seyed Ehsan Shakib, “Cost optimization of a combined power and waterdesalination plant with exergetic, environment and reliability consideration”, Desalination, Vol. 285, pp. 123–130,January 31, 2012.

325. Juan F. Fernandez-Bootello, Manuel Delgado-Restituto and Angel Rodriguez-Vazquez, “IC-constrained optimization ofcontinuous-time Gm-C filters”, International Journal of Circuit Theory and Applications, Vol. 40, No. 2, pp. 127–143,February 2012.

326. Jose Luis Guerrero, Antonio Berlanga and Jose Manuel Molina, “A multi-objective approach for the segmentation issue”,Engineering Optimization, Vol. 44, No. 3, pp. 267–287, 2012.

327. A.M. Mora, P. Garcia-Sanchez, J.J. Merelo and P.A. Castillo, “Pareto-based multi-colony multi-objective ant colonyoptimization algorithms: an island model proposal”, Soft Computing, Vol. 17, No. 7, pp. 1175–1207, July 2013.

328. Cristina Teixeira, J.A. Covas, Thomas Stutzle and A. Gaspar-Cunha, “Multi-objective ant colony optimization for thetwin-screw configuration problem”, Engineering Optimization, Vol. 44, No. 3, pp. 351–371, 2012.

329. K. Sivakumar, C. Balamurugan and S. Ramabalan, “Evolutionary multi-objective concurrent maximisation of processtolerances”, International Journal of Production Research, Vol. 50, No. 12, pp. 3172–3191, 2012.

330. X. Dong, S. Zeng and J. Chen, “A spatial multi-objective optimization model for sustainable urban wastewater systemlayout planning”, Water Science and Technology, Vol. 66, No. 2, pp. 267–274, 2012.

331. Jaime Gagne and Marilyne Andersen, “A generative facade design method based on daylighting performance goals”,Journal of Building Performance Simulation, Vol. 5, No. 3, pp. 141–154, 2012.

332. Amos H.C. Ng, Jacob Bernedixen and Anna Syberfeldt, “A comparative study of production control mechanisms usingsimulation-based multi-objective optimisation”, International Journal of Production Research, Vol. 50, No. 2, pp.359–377, 2012.

333. Susmita Bandyopadhyay and Ranjan Bhattacharya, “Applying modified NSGA-II for bi-objective supply chain problem”,Journal of Intelligent Manufacturing, Vol. 24, No. 4, pp. 707–716, August 2013.

334. J.B. Kollat, P.M. Reed and T. Wagener, “When are multiobjective calibration trade-offs in hydrologic models meaning-ful?”, Water Resources Research, Vol. 48, Article Number: W03520, March 21, 2012.

335. Miguel G. Villarreal-Cervantes, Carlos A. Cruz-Villar, Jaime Alvarez-Gallegos and Edgar A. Portilla-Flores, “RobustStructure-Control Design Approach for Mechatronic Systems”, IEEE-ASME Transactions on Mechatronics, Vol. 18,No. 5, pp. 1592–1601, October 2013.

336. Masoud Asadzadeh and Bryan Tolson, “Hybrid Pareto archived dynamically dimensioned search for multi-objectivecombinatorial optimization: application to water distribution network design”, Journal of Hydroinformatics, Vol. 14,No. 1, pp. 192–205, January 2012.

337. Jeroen Groot and Walter A.H. Rossing, “Model-aided learning for adaptive management of natural resources: an evolu-tionary design perspective”, Methods in Ecology and Evolution, Vol. 2, No. 6, pp. 643–650, December 2011.

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338. Viktor Vegh and Quang M. Tieng, “Unconstrained Real Valued Optimization Based on Stochastic Differential Equa-tions”, International Journal of Innovative Computing Information and Control, Vol. 7, No. 11, pp. 6235–6246,November 2011.

339. Andre L.V. Coelho, Everlandio Fernandes and Katti Faceli, “Multi-objective design of hierarchical consensus functionsfor clustering ensembles via genetic programming”, Decision Support Systems, Vol. 51, No. 4, pp. 794–809, November2011.

340. J.C. Calvo, J. Ortega and M. Anguita, “Comparison of parallel multi-objective approaches to protein structure predic-tion”, Journal of Supercomputing, Vol. 58, No. 2, pp. 253–260, November 2011.

341. E. Zio and G. Viadana, “Optimization of the inspection intervals of a safety system in a nuclear power plant by Multi-Objective Differential Evolution (MODE)”, Reliability Engineering & System Safety, Vol. 96, No. 11, pp. 1552–1563,November 2011.

342. Yufeng Shang and Bo Yu, “A constraint shifting homotopy method for convex multi-objective programming”, Journalof Computational and Applied Mathematics, Vol. 236, No. 5, pp. 640–646, October 1, 2011.

343. Satyabrata Sen, Gongguo Tang and Arye Nehorai, “Multiobjective Optimization of OFDM Radar Waveform for TargetDetection”, IEEE Transactions on Signal Processing, Vol. 59, No. 2, pp. 639–652, February 2011.

344. Y.-K. Juan, L. Wang, J. Wang, J.O. Leckie and K.-M. Li, “A decision-support system for smarter city planning andmanagement”, IBM Journal of Research and Development, Vol. 55, Nos. 1-2, Article Number: 3, January-March 2011.

345. Bara’a Ali Attea, Laylan Mohammad Rashid and Wafaa Khazzal Shames, “Evolutionary algorithm for example-basedpainterly rendering”, International Journal of Bio-Inspired Computation, Vol. 2, No. 2, pp. 132–141, 2010.

346. Konstantinos B. Baltzis, “An Efficient Finger Allocation Method for the Maximum Likelihood RAKE Receiver”, Radio-engineering, Vol. 17, No. 4, pp. 45–50, December 2008.

347. Claudio R.M. Silva and Sinara R. Martins, “An Adaptive Evolutionary Algorithm for UWB Microstrip Antennas Op-timization using a Machine Learning Technique”, Microwave and Optical Technology Letters, Vol. 55, No. 8, pp.1864–1868, August 2013.

348. Steve Bergen and Brian J. Ross, “Aesthetic 3D model evolution”, Genetic Programming and Evolvable Machines, Vol.14, No. 3, pp. 339–367, September 2013.

349. Leandro D. Vignolo, Diego H. Milone and Jacob Scharcanski, “Feature selection for face recognition based on multi-objective evolutionary wrappers”, Expert Systems with Applications, Vol. 40, No. 13, pp. 5077–5084, October 1, 2013.

350. Weijian Kong, Tianyou Chai, Shengxiang Yang and Jinliang Ding, “A hybrid evolutionary multiobjective optimizationstrategy for the dynamic power supply problem in magnesia grain manufacturing”, Applied Soft Computing, Vol. 13,No. 5, pp. 2960–2969, May 2013.

351. Sultan Nomal Qasem, Siti Mariyam Shamsuddin, Siti Zaiton Mohd Hashim, Maslina Darus and Eiman Al-Shammari,“Memetic multiobjective particle swarm optimization-based radial basis function network for classification problems”,Information Sciences, Vol. 239, pp. 165–190, August 1, 2013.

352. Hossein Rajabalipour Cheshmehgaz, Mohamad Ishak Desa and Antoni Wibowo, “A flexible three-level logistic net-work design considering cost and time criteria with a multi-objective evolutionary algorithm”, Journal of IntelligentManufacturing, Vol. 24, No. 2, pp. 277–293, April 2013.

353. M. Rabiee, M. Zandieh and P. Ramezani, “Bi-objective partial flexible job shop scheduling problem: NSGA-II, NRGA,MOGA and PAES approaches”, International Journal of Production Research, Vol. 50, No. 24, pp. 7327–7342, 2012.

354. Prakash Shelokar, Arnaud Quirin and Oscar Cordon, “A multiobjective evolutionary programming framework for graph-based data mining”, Information Sciences, Vol. 237, pp. 118–136, July 10, 2013.

355. Christiane Regina Soares Brasil, Alexandre Claudio Botazzo Delbem and Fernando Luis Barroso da Silva, “Multiobjectiveevolutionary algorithm with many tables for purely ab initio protein structure prediction”, Journal of ComputationalChemistry, Vol. 34, No. 20, pp. 1719–1734, July 30, 2013.

356. Vui Ann Shim, Kay Chen Tan, Jun Yong Chia and Abdullah Al Mamun, “Multi-Objective Optimization with Estimationof Distribution Algorithm in a Noisy Environment”, Evolutionary Computation, Vol. 21, No. 1, pp. 149–177, Spring2013.

357. Sinan Korkmaz, Nizar Bel Hadj Ali and Ian F.C. Smith, “Configuration of control system for damage tolerance of atensegrity bridge”, Advanced Engineering Informatics, Vol. 26, No. 1, pp. 145–155, January 2012.

358. Eunice Oliveira, Carlos Henggeler Antunes and Alvaro Gomes, “A comparative study of different approaches using anoutranking relation in a multi-objective evolutionary algorithm”, Computers & Operations Research, Vol. 40, No. 6, pp.1602–1615, June 2013.

359. Alexandre C.B. Delbem, Telma W. de Lima and Guilherme P. Telles, “Efficient Forest Data Structure for EvolutionaryAlgorithms Applied to Network Design”, IEEE Transactions on Evolutionary Computation, Vol. 16, No. 6, pp. 829–846,December 2012.

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360. Marcia P. Basgalupp, Andre C.P.L.F. de Carvalho, Rodrigo C. Barros, Duncan D. Ruiz and Alex A. Freitas, “Lexico-graphic multi-objective evolutionary induction of decision trees”, International Journal of Bio-Inspired Computation,Vol. 1, Nos. 1-2, pp. 105–117, 2009.

361. M.V.C. da Silva, N. Nedjah and L.M. Mourelle, “Power-aware multi-objective evolutionary optimisation for applicationmapping on network-on-chip platforms”, International Journal of Electronics, Vol. 97, No. 10, pp. 1163–1179, ArticleNumber: PII 927691877, 2010.

362. Maria del Jesus, Jose A. Gamez, Pedro Gonzalez and Jose M. Puerta, “On the discovery of association rules by meansof evolutionary algorithms”, Wiley Interdisciplinary Reviews–Data Mining and Knowlegde Discovery, Vol. 1, No. 5, pp.397–415, September-October 2011.

363. Xinye Cai, Ou Wei and Zhiqiu Huang, “Evolutionary Approaches for Multi-Objective Next Release Problem”, Computingand Informatics, Vol. 31, No. 4, pp. 847–875, 2012.

364. Marcela Zuluaga, Andreas Krause, Peter Milder and Markus Puschel, ““Smart” Design Space Sampling to PredictPareto-Optimal Solutions”, ACM SIGPLAN Notices, Vol. 47, No. 5, pp. 119–128, May 2012.

365. Matthieu Basseur, Rong-Qiang Zeng and Jin-Kao Hao, “Hypervolume-based multi-objective local search”, Neural Com-puting & Applications, Vol. 21, No. 8, pp. 1917–1929, November 2012.

366. Diana Manjarres, Javier Del Ser, Sergio Gil-Lopez, Massimo Vecchio, Itziar Landa-Torres, Sancho Salcedo-Sanz andRoberto Lopez-Valcarce, “On the design of a novel two-objective harmony search approach for distance- and connectivity-based localization in wireless sensor networks”, Engineering Applications of Artificial Intelligence, Vol. 26, No. 2, pp.669–676, February 2013.

367. Nuria Macia, Ester Bernado-Mansilla, Albert Orriols-Puig and Tin Kam Ho, “Learner excellence biased by data setselection: A case for data characterisation and artificial data sets”, Pattern Recognition, Vol. 46, No. 3, pp. 1054–1066,March 2013.

368. Thelma Elita Colanzi, Silvia Regina Vergilio, Wesley Klewerton Guez Assuncao and Aurora Pozo, “Search Based SoftwareEngineering: Review and analysis of the field in Brazil”, Journal of Systems and Software, Vol. 86, No. 4, pp. 970–984,April 2013.

369. Francisco J. Rodriguez, Carlos Garcia-Martinez and Manuel Lozano, “Hybrid Metaheuristics Based on EvolutionaryAlgorithms and Simulated Annealing: Taxonomy, Comparison, and Synergy Test”, IEEE Transactions on EvolutionaryComputation, Vol. 16, No. 6, pp. 787–800, December 2012.

370. David Hadka and Patrick Reed, “Borg: An Auto-Adaptive Many-Objective Evolutionary Computing Framework”,Evolutionary Computation, Vol. 21, No. 2, pp. 231–259, Summer 2013.

371. Ronay Ak, Yanfu Li, Valeria Vitelli, Enrico Zio, Enrique Lopez Droguett and Carlos Magno Couto Jacinto, “NSGA-II-trained neural network approach to the estimation of prediction intervals of scale deposition rate in oil & gas equipment”,Expert Systems with Applications, Vol. 40, No. 4, pp. 1205–1212, March 2013.

372. Sergio Nesmachnow, “Parallel multiobjective evolutionary algorithms for batch scheduling in heterogeneous computingand grid systems”, Computational Optimization and Applications, Vol. 55, No. 2, pp. 515–544, June 2013.

373. Vili Podgorelec, Matej Sprogar and Sandi Pohorec, “Evolutionary design of decision trees”, Wiley InterdisciplinaryReviews-Data Mining and Knowledge Discovery, Vol. 3, No. 2, pp. 63–82, March-April 2013.

374. Wanxing Sheng, Ke-yan Liu, Yongmei Liu, Xiaoli Meng and Xiaohui Song, “A New DG Multiobjective OptimizationMethod Based on an Improved Evolutionary Algorithm”, Journal of Applied Mathematics, Article Number: 643791,2013.

375. Raul Banos, Julio Ortega, Consolacion Gil, Antonio L. Marquez and Francisco de Toro, “A hybrid meta-heuristic formulti-objective vehicle routing problems with time windows”, Computers & Industrial Engineering, Vol. 65, No. 2, pp.286–296, June 2013.

376. David Lahoz, Beatriz Lacruz and Pedro M. Mateo, “A multi-objective micro genetic ELM algorithm”, Neurocomputing,Vol. 111, pp. 90–103, July 2, 2013.

377. Giuseppe Aiello, Giada La Scalia and Mario Enea, “A non dominated ranking Multi Objective Genetic Algorithm andelectre method for unequal area facility layout problems”, Expert Systems with Applications, Vol. 40, No. 12, pp.4812–4819, September 15, 2013.

378. Raffaele Grasso, Marco Cococcioni, Baptiste Mourre, John Osler and Jacopo Chiggiato, “A decision support system foroptimal deployment of sonobuoy networks based on sea current forecasts and multi-objective evolutionary optimization”,Expert Systems with Applications, Vol. 40, No. 10, pp. 3886–3899, August 2013.

379. Bo Wang and Junzo Watada, “Multiobjective particle swarm optimization for a novel fuzzy portfolio selection problem”,IEEJ Transactions on Electrical and Electronic Engineering, Vol. 8, No. 2, pp. 146–154, March 2013.

380. Andre Schardong, Slobodan P. Simonovic and A. Vasan, “Multiobjective Evolutionary Approach to Optimal ReservoirOperation”, Journal of Computing in Civil Engineering, Vol. 27, No. 2, pp. 139–147, March 2013.

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381. Maoguo Gong, Xiaowei Chen, Lijia Ma, Qingfu Zhang and Licheng Jiao, “Identification of multi-resolution networkstructures with multi-objective immune algorithm”, Applied Soft Computing, Vol. 13, No. 4, pp. 1705–1717, April 2013.

382. A. Jamali, M. Ghamati, B. Ahmadi and N. Nariman-zadeh, “Probability of failure for uncertain control systems us-ing neural networks and multi-objective uniform-diversity genetic algorithms (MUGA)”, Engineering Applications ofArtificial Intelligence, Vol. 26, No. 2, pp. 714–723, February 2013.

383. Fabien Tricoire, “Multi-directional local search”, Computers & Operations Research, Vol. 39, No. 12, pp. 3089–3101,December 2012.

384. Gilberto Reynoso-Meza, Sergio Garcia-Nieto, Javier Sanchis and F. Xavier Blasco, “Controller Tuning by Means of Multi-Objective Optimization Algorithms: A Global Tuning Framework”, IEEE Transactions on Control Systems Technology,Vol. 21, No. 2, pp. 445–458, March 2013.

385. A. Kaveh and K. Laknejadi, “A hybrid evolutionary graph-based multi-objective algorithm for layout optimization oftruss structures”, Acta Mechanica, Vol. 224, No. 2, pp. 343–364, February 2013.

386. Rajan Filomeno Coelho, “Co-Evolutionary Optimization for Multi-Objective Design Under Uncertainty”, Journal ofMechanical Design, Vol. 135, No. 2, Article Number: 021006, February 2013.

387. Igor Vatolkin, Mike Preuss, Gunter Rudolph, Markus Eichhoff and Claus Weihs, “Multi-objective evolutionary featureselection for instrument recognition in polyphonic audio mixtures”, Soft Computing, Vol. 16, No. 12, pp. 2027–2047,December 2012.

388. Anirban Mukhopadhyay, Sumanta Ray and Moumita De, “Detecting protein complexes in a PPI network: a geneontology based multi-objective evolutionary approach”, Molecular Biosystems, Vol. 8, No. 11, pp. 3036–3048, 2012.

389. A. Alcayde, R. Banos, C. Gil, F.G. Montoya, J. Moreno-Garcia and J. Gomez, “Annealing-tabu PAES: a multi-objectivehybrid meta-heuristic”, Optimization, Vol. 60, No. 12, pp. 1473–1491, 2011.

390. Raul Banos, Julio Ortega, Consolacion Gil, Antonio Fernandez and Francisco de Toro, “A Simulated Annealing-basedparallel multi-objective approach to vehicle routing problems with time windows”, Expert Systems with Applications,Vol. 40, No. 5, pp. 1696–1707, April 2013.

391. Gilberto Reynoso-Meza, Xavier Blasco, Javier Sanchis and Juan M. Herrero, “Comparison of design concepts in multi-criteria decision-making using level diagrams”, Information Sciences, Vol. 221, pp. 124–141, February 1, 2013.

392. Michela Fazzolari, Rafael Alcala, Yusuke Nojima, Hisao Ishibuchi and Francisco Herrera, “A Review of the Applicationof Multiobjective Evolutionary Fuzzy Systems: Current Status and Further Directions”, IEEE Transactions on FuzzySystems, Vol. 21, No. 1, pp. 45–65, February 2013.

393. Maria Jose Gacto, Rafael Alcala and Francisco Herrera, “A multi-objective evolutionary algorithm for an effective tuningof fuzzy logic controllers in heating, ventilating and air conditioning systems”, Applied Intelligence, Vol. 36, No. 2, pp.330–347, March 2012.

394. Ke Li, Sam Kwong, Ran Wang, Kit-Sang Tang and Kim-Fung Man, “Learning paradigm based on jumping genes: Ageneral framework for enhancing exploration in evolutionary multiobjective optimization”, Information Sciences, Vol.226, pp. 1–22, March 20, 2013.

395. P.M. Reed, D. Hadka, J.D. Herman, J.R. Kasprzyk and J.B. Kollat, “Evolutionary multiobjective optimization in waterresources: The past, present, and future”, Advances in Water Resources, Vol. 51, pp. 438–456, January 2013.

396. Lixin Tang and Xianpen Wang, “A Hybrid Multiobjective Evolutionary Algorithm for Multiobjective OptimizationProblems”, IEEE Transactions on Evolutionary Computation, Vol. 17, No. 1, pp. 20–45, February 2013.

397. Juan Arturo Herrera Ortiz, Katya Rodriguez-Vazquez, Miguel A. Castaneda and Fernando Arambula Cosio, “Au-tonomous robot navigation based on the evolutionary multi-objective optimization of potential fields”, EngineeringOptimization, Vol. 45, No. 1, pp. 19–43, 2013.

398. Sultan Noman Qasem, Siti Mariyam Shamsuddin and Azlan Mohd Zain, “Multi-objective hybrid evolutionary algorithmsfor radial basis function neural network design”, Knowledge-based Systems, Vol. 27, pp. 475–497, March 2012.

399. M.J. Mahmoodabadi, S. Arabani Mostaghim, A. Bagheri and N. Nariman-zadeh, “Pareto optimal design of the de-coupled sliding mode controller for an inverted pendulum system and its stability simulation via Java programming”,Mathematical and Computer Modelling, Vol. 57, Nos. 5-6, pp. 1070–1082, March 2013.

400. M.J. Mahmoodabadi, A. Bagheri, N. Nariman-Zadeh, A. Jamali and R. Abedzadeh Maafi, “Pareto Design of DecoupledSliding-Mode Controllers for Nonlinear Systems Based on a Multiobjective Genetic Algorithm”, Journal of AppliedMathematics, Article Number: 639014, 2012.

401. A. Shokuhi-Rad, A. Jamali, M. Naghashzadegan, N. Nariman-zadeh and A. Hajiloo, “Optimum Pareto design of non-linear predictive control with multi-design variables for PEM fuel cell”, International Journal of Hydrogen Energy, Vol.37, No. 15, pp. 11244–11254, August 2012.

402. M. Mohammad Rezapour Tabari and Jaber Soltani, “Multi-Objective Optimal Model for Conjunctive Use ManagementUsing SGAs and NSGA-II Models”, Water Resources Management, Vol. 27, No. 1, pp. 37–53, January 2013.

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403. Itza T.Q. Curiel, Sonia B. Di Giannatale, Juan A. Herrera and Katya Rodriguez, “Pareto Frontier of a DynamicPrincipal-Agent Model with Discrete Actions: An Evolutionary Multi-Objective Approach”, Computational Economics,Vol. 40, No. 4, pp. 415–443, December 2012.

404. Fernando Alonso Zotes and Matilde Santos Penas, “Particle swarm optimisation of interplanetary trajectories from Earthto Jupiter and Saturn”, Engineering Applications of Artificial Intelligence, Vol. 25, No. 1, pp. 189–199, February 2012.

405. Virginia Yannibelli and Analia Amandi, “Project scheduling: A multi-objective evolutionary algorithm that optimizesthe effectiveness of human resources and the project makespan”, Engineering Optimization, Vol. 45, No. 1, pp. 45–65,2013.

406. Carolina P. Almeida, Richard A. Goncalves, Elizabeth F. Goldbarg, Marco C. Goldbarg and Myriam R. Delgado, “Anexperimental analysis of evolutionary heuristics for the biobjective traveling purchaser problem”, Annals of OperationsResearch, Vol. 199, No. 1, pp. 305–341, October 2012.

407. Arnaud Liefooghe, Matthieu Basseur, Jeremie Humeau, Laetitia Jourdan and El-Ghazali Talbi, “On optimizing a bi-objective flowshop scheduling problem in an uncertain environment”, Computers & Mathematics with Applications, Vol.64, No. 12, pp. 3747–3762, December 2012.

408. Christian Grimme, Joachim Lepping and Alexander Papaspyrou, “Parallel predator-prey interaction for evolutionarymulti-objective optimization”, Natural Computing, Vol. 11, No. 3, pp. 519–533, September 2012.

409. Arnaud Liefooghe, Jeremie Humeau, Salma Mesmoudi, Laetitia Jourdan and El-Ghazali Talbi, “On dominance-basedmultiobjective local search: design, implementation and experimental analysis on scheduling and traveling salesmanproblems”, Journal of Heuristics, Vol. 18, No. 2, pp. 317–352, April 2012.

410. David Greiner and Prabhat Hajela, “Truss topology optimization for mass and reliability considerations-co-evolutionarymultiobjective formulations”, Structural and Multidisciplinary Optimization, Vol. 45, No. 4, pp. 589–613, April 2012.

411. Thomas Weise, Raymond Chiong and Ke Tang, “Evolutionary Optimization: Pitfalls and Booby Traps”, Journal ofComputer Science and Technology, Vol. 27, No. 5, pp. 907–936, September 2012.

412. Jose A. Salinas-Perez, Carlos R. Garcia-Alonso, Cristina Molina-Parrilla, Esther Jorda-Sampietro and Luis Salvador-Carulla, “Identification and location of hot and cold spots of treated prevalence of depression in Catalonia (Spain)”,International Journal of Health Geographics, Vol. 11, Article Number: 36, August 24, 2012.

413. Elisenda Roca, Manuel Velasco-Jimenez, Rafael Castro-Lopez and Francisco V. Fernandez, “Context-dependent transfor-mation of Pareto-optimal performance fronts of operational amplifiers”, Analog Integrated Circuits and Signal Processing,Vol. 73, No. 1, pp. 65–76, October 2012.

414. Neda Manavizadeh, Masoud Rabbani, Davoud Moshtaghi and Fariborz Jolai, “Mixed-model assembly line balancingin the make-to-order and stochastic environment using multi-objective evolutionary algorithms”, Expert Systems withApplications, Vol. 39, No. 15, pp. 12026–12031, November 1, 2012.

415. Enrique Alba, Gabriel Luque and Sergio Nesmachnow, “Parallel metaheuristics: recent advances and new trends”,International Transactions in Operational Research, Vol. 20, No. 1, pp. 1–48, January 2013.

416. Soumyadip Sengupta, Swagatam Das, Md Nasir, Athanasios V. Vasilakos and Witold Pedrycz, “An Evolutionary Mul-tiobjective Sleep-Scheduling Scheme for Differentiated Coverage in Wireless Sensor Networks”, IEEE Transactions onSystems, Man and Cybernetics Part C–Applications and Reviews, Vol. 42, No. 6, pp. 1093–1102, November 2012.

417. Isabelle Grechi, Mohamed-Mahmoud Ould-Sidi, Nadine Hilgert, Rachid Senoussi, Benoit Sauphanor and FrancoiseLescourret, “Designing integrated management scenarios using simulation-based and multi-objective optimization: Ap-plication to the peach tree-Myzus persicae aphid system”, Ecological Modelling, Vol. 246, pp. 47–59, November 10,2012.

418. Patricia Ruiz, Bernabe Dorronsoro, Giorgio Valentini, Frederic Pinel and Pascal Bouvry, “Optimisation of the enhanceddistance based broadcasting protocol for MANETs”, Journal of Supercomputing, Vol. 62, No. 3, pp. 1213–1240,December 2012.

419. I. Arnaldo, J.L. Risco-Martin, J.L. Ayala and J.I. Hidalgo, “Power profiling-guided floorplanner for 3D multi-processorsystems-on-chip”, IET Circuits Devices & Systems, Vol. 6, No. 5, pp. 322–329, September 2012.

420. Gift Dumedah, “Formulation of the Evolutionary-Based Data Assimilation, and its Implementation in HydrologicalForecasting”, Water Resources Management, Vol. 26, No. 13, pp. 3853–3870, October 2012.

421. Clare Levene, Elon Correa, Ewan W. Blanch and Royston Goodacre, “Enhancing Surface Enhanced Raman Scattering(SERS) Detection of Propranolol with Multiobjective Evolutionary Optimization”, Analytical Chemistry, Vol. 84, No.18, pp. 7899–7905, September 18, 2012.

422. Kalyanmoy Deb, Francisco Ruiz, Mariano Luque, Rahul Tewari, Jose M. Cabello and Jose M. Cejudo, “On the sizing ofa solar thermal electricity plant for multiple objectives using evolutionary optimization”, Applied Soft Computing, Vol.12, No. 10, pp. 3300–3311, October 2012.

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423. Gift Dumedah, Aaron A. Berg and Mark Wineberg, “Pareto-optimality and a search for robustness: choosing solutionswith desired properties in objective space and parameter space”, Journal of Hydroinformatics, Vol. 14, No. 2, pp.270–285, 2012.

424. James N. Richardson, Sigrid Adriaenssens, Philippe Bouillard and Rajan Filomeno Coelho, “Multiobjective topologyoptimization of truss structures with kinematic stability repair”, Structural and Multidisciplinary Optimization, Vol. 46,No. 4, pp. 513–532, October 2012.

425. Chun-Hao Chen, Tzung-Pei Hong, Vincent S. Tseng and Lien-Chin Chen, “Multi-objective Genetic-Fuzzy Data Mining”,International Journal of Innovative Computing Information and Control, Vol. 8, No. 10A, pp. 6551–6568, October 2012.

426. P.M. Mateo and I. Alberto, “A mutation operator based on a Pareto ranking for multi-objective evolutionary algorithms”,Journal of Heuristics, Vol. 18, No. 1, pp. 53–89, February 2012.

427. M.R. Dashtbayazi, “Artificial Neural Network-Based Multiobjective Optimization of Mechanical Alloying Process forSynthesizing of Metal Matrix Nanocomposite Powder”, Materials and Manufacturing Processes, Vol. 27, No. 1, pp.33–42, 2012.

428. H. Nasiraghdam and S. Jadid, “Optimal hybrid PV/WT/FC sizing and distribution system reconfiguration using multi-objective artificial bee colony (MOABC) algorithm”, Solar Energy, Vol. 86, No. 10, pp. 3057–3071, October 2012.

429. Aryeh Warmflash, Paul Francois and Eric D. Siggia, “Pareto evolution of gene networks: an algorithm to optimizemultiple fitness objectives”, Physical Biology, Vol. 9, No. 5, Article Number: 056001, October 2012.

430. Manuel Chica, Oscar Cordon, Sergio Damas and Joaquin Bautista, “Multiobjective memetic algorithms for time andspace assembly line balancing”, Engineering Applications of Artificial Intelligence, Vol. 25, No. 2, pp. 254–273, March2012.

431. E. Zio, L.R. Golea and C.M. Rocco, “Identifying groups of critical edges in a realistic electrical network by multi-objectivegenetic algorithms”, Reliability Engineering & System Safety, Vol. 99, pp. 172–177, March 2012.

432. G. Chiandussi, M. Codegone, S. Ferrero and F.E. Varesio, “Comparison of multi-objective optimization methodologiesfor engineering applications”, Computers & Mathematics with Applications, Vol. 63, No. 5, pp. 912–942, March 2012.

433. Nuno F. Lages, Carlos Cordeiro, Marta Sousa Silva, Ana Ponces Freire and Antonio E.N. Ferreira, “Optimization ofTime-Course Experiments for Kinetic Model Discrimination”, Plos One, Vol. 7, No. 3, Article Number: e32749, March5, 2012.

434. Samane Noori-Darvish, Iraj Mahdavi and Nezam Mahdavi-Amiri, “A bi-objective possibilistic programming model foropen shop scheduling problems with sequence-dependent setup times, fuzzy processing times, and fuzzy due dates”,Applied Soft Computing, Vol. 12, No. 4, pp. 1399–1416, April 2012.

435. Sahar Ashayer, Mansur Askari and Hossein Afarideh, “Optimal per cent by weight of elements in diagnostic qualityradiation shielding materials”, Radiation Protection Dosimetry, Vol. 149, No. 3, pp. 268–288, April 2012.

436. Manojkumar Ramteke and Rajagopalan Srinivasan, “Large-Scale Refinery Crude Oil Scheduling by Integrating GraphRepresentation and Genetic Algorithm”, Industrial & Engineering Chemistry Research, Vol. 51, No. 14, pp. 5256–5272,April 11, 2012.

437. Izaskun Ibarbia, Alexander Mendiburu, Maria Santos and Jose A. Lozano, “An interactive optimization approach to areal-world oceanographic campaign planning problem”, Applied Intelligence, Vol. 36, No. 3, pp. 721–734, April 2012.

438. Weiqin Ying, Xing Xu, Yuxiang Feng and Yu Wu, “An Efficient Conical Area Evolutionary Algorithm for Bi-objective Op-timization”, IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, Vol. E95A,No. 8, pp. 1420–1425, August 2012.

439. Roberto Santana, Concha Bielza and Pedro Larranaga, “Regularized logistic regression and multiobjective variableselection for classifying MEG data”, Biological Cybernetics, Vol. 106, Nos. 6–7, pp. 389–405, September 2012.

440. Carolina P. Almeida, Richard A. Goncalves, Elizabeth F. Goldbarg, Marco C. Goldbarg and Myriam R. Delgado, “Anexperimental analysis of evolutionary heuristics for the biobjective traveling purchaser problem”, Annals of OperationsResearch, Vol. 199, No. 1, pp. 305–341, October 2012.

441. Jun-fang Li, Bu-han Zhang, Yi-fang Liu, Kui Wang and Xiao-shan Wu, “Spatial evolution character of multi-objectiveevolutionary algorithm based on self-organized criticality theory”, Physica A–Statistical Mechanics and its Applications,Vol. 391, No. 22, pp. 5490–5499, November 15, 2012.

442. Ofer M. Shir, Jonathan Roslund, Zaki Leghtas and Herschel Rabitz, “Quantum control experiments as a testbed forevolutionary multi-objective algorithms”, Genetic Programming and Evolvable Machines, Vol. 13, No. 4, pp. 445–491,December 2012.

443. Domenico A. Bau and Jonghyun Lee, “Multi-Objective Optimization for the Design of Groundwater Supply Systemsunder Uncertain Parameter Distribution”, Pacific Journal of Optimization, Vol. 7, No. 3, pp. 407–424, September 2011.

444. Hugo-Tiago C. Pedro and Marcelo H. Kobayashi, “On a cellular division method for topology optimization”, InternationalJournal for Numerical Methods in Engineering, Vol. 88, No. 11, pp. 1175–1197, December 16, 2011.

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445. Domenico A. Bau, “Planning of Groundwater Supply Systems Subject to Uncertainty Using Stochastic Flow ReducedModels and Multi-Objective Evolutionary Optimization”, Water Resources Management, Vol. 26, No. 9, pp. 2513–2536,July 2012.

446. Anthony Gerard Scanlan and Mark Keith Halton, “Hierarchical synthesis system with hybrid DLO-MOGA optimization”,COMPEL–The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, Vol.30, No. 2, pp. 741–761, 2011.

447. Gustavo Olague and Leonardo Trujillo, “Interest point detection through multiobjective genetic programming”, AppliedSoft Computing, Vol. 12, No. 8, pp. 2566–2582, August 2012.

448. A. Clarke and J.C. Miles, “Strategic Fire and Rescue Service decision making using evolutionary algorithms”, Advancesin Engineering Software, Vol. 50, pp. 29–36, August 2012.

449. Vicent Romero-Garcia, Juan Sanchez-Perez and Luis Miguel Garcia-Raffi, “Molding the Acoustic Attenuation in Quasi-Ordered Structures: Experimental Realization”, Applied Physics Express, Vol. 5, No. 8, Article Number: 087301,August 2012.

450. Pankaj Rajak, Sudipto Ghosh, Baidurya Bhattacharya and Nirupam Chakraborti, “Pareto-optimal analysis of Zn-coatedFe in the presence of dislocations using genetic algorithms”, Computational Materials Science, Vol. 62, pp. 266–271,September 2012.

451. Benedicte Quilot-Turion, Mohamed-Mahmoud Ould-Sidi, Abdeslam Kadrani, Nadine Hilgert, Michel Genard and Fran-coise Lescourret, “Optimization of parameters of the ‘Virtual Fruit’ model to design peach genotype for sustainableproduction systems”, European Journal of Agronomy, Vol. 42, pp. 34–48, October 2012.

452. David Hadka and Patrick Reed, “Diagnostic Assessment of Search Controls and Failure Modes in Many-ObjectiveEvolutionary Optimization”, Evolutionary Computation, Vol. 20, No. 3, pp. 423–452, Fall 2012.

453. Anne Auger, Johannes Bader, Dimo Brockhoff and Eckart Zitzler, “Hypervolume-based multiobjective optimization:Theoretical foundations and practical implications”, Theoretical Computer Science, Vol. 425, pp. 75–103, March 30,2012.

454. Wali Khan Mashwani and Abdellah Salhi, “A decomposition-based hybrid multiobjective evolutionary algorithm withdynamic resource allocation”, Applied Soft Computing, Vol. 12, No. 9, pp. 2765–2780, September 2012.

455. F.R.B. Cruz, G. Kendall, L. While, A.R. Duarte and N.L.C. Brito, “Throughput Maximization of Queueing Networkswith Simultaneous Minimization of Service Rates and Buffers”, Mathematical Problems in Engineering, Article Number:692593, 2012.

456. Rodrigo Coelho Barros, Marcio Porto Basgalupp, Andre C.P.L.F. de Carvalho and Alex A. Freitas, “A Survey ofEvolutionary Algorithms for Decision-Tree Induction”, IEEE Transactions on Systems, Man and Cybernetics Part C–Applications and Reviews, Vol. 42, No. 3, pp. 291–312, May 2012.

457. Reinhard Koenig and Sven Schneider, “Hierarchical structuring of layout problems in an interactive evolutionary layoutsystem”, AI EDAM-Artificial Intelligence for Engineering Design Analysis and Manufacturing, Vol. 26, No. 2, pp.129–142, May 2012.

458. Clara Pizzuti, “A Multiobjective Genetic Algorithm to Find Communities in Complex Networks”, IEEE Transactionson Evolutionary Computation, Vol. 16, No. 3, pp. 418–430, June 2012.

459. Amelia Zafra and Sebastian Ventura, “Multi-objective approach based on grammar-guided genetic programming forsolving multiple instance problems”, Soft Computing, Vol. 16, No. 6, pp. 955–977, June 2012.

460. Kaveh Khalili-Damghani abnd Maghsoud Amiri, “Solving binary-state multi-objective reliability redundancy allocationseries-parallel problem using efficient epsilon-constraint, multi-start partial bound enumeration algorithm, and DEA”,Reliability Engineering & System Safety, Vol. 103, pp. 35–44, July 2012.

461. Davide Bianchi, Simone Genovesi and Agostino Monorchio, “Constrained Pareto Optimization of Wide Band and Steer-able Concentric Ring Arrays”, IEEE Transactions on Antennas and Propagation, Vol. 60, No. 7, pp. 3195–3204, July2012.

462. Renan S. Maciel, Mauro Rosa, Vladimiro Miranda and Antonio Padilha-Feltrin, “Multi-objective evolutionary particleswarm optimization in the assessment of the impact of distributed generation”, Electric Power Systems Research, Vol.89, pp. 100–108, August 2012.

463. Satoshi Kitayama and Koetsu Yamazaki, “Compromise point incorporating trade-off ratio in multi-objective optimiza-tion”, Applied Soft Computing, Vol. 12, No. 8, pp. 1959–1964, August 2012.

464. Manuel Cruz-Ramirez, Cesar Hervas-Martinez, Juan Carlos Fernandez, Javier Briceno and Manuel de la Mata, “Multi-objective evolutionary algorithm for donor-recipient decision system in liver transplants”, European Journal of Opera-tional Research, Vol. 222, No. 2, pp. 317–327, October 16, 2012.

465. C. Voglis, K.E. Parsopoulos, D.G. Papageorgiou, I.E. Lagaris and M.N. Vrahatis, “MEMPSODE: A global optimizationsoftware based on hybridization of population-based algorithms and local searches”, Computer Physics Communications,Vol. 183, No. 5, pp. 1139–1154, May 2012.

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466. C. Fernandes, A.J. Pontes, J.C. Viana and A. Gaspar-Cunha, “Using Multi-objective Evolutionary Algorithms forOptimization of the Cooling System in Polymer Injection Molding”, International Polymer Processing, Vol. 27, No. 2,pp. 213–223, May 2012.

467. Gheorghe Serban, Laurentiu Ionescu and Alin Mazare, “The Possibility of Optimisation for Power Supply Consumptionusing Evolvable Power Regulator”, Revue Roumaine des Sciences Techniques–Serie Electrotechnique et Energetique, Vol.57, No. 2, pp. 222–231, April-June 2012.

468. Fangqing Gu, Hai-lin Liu and Kay Chen Tan, “A Multiobjective Evolutionary Algorithm using Dynamic Weight DesignMethod”, International Journal of Innovative Computing Information and Control, Vol. 8, No. 5B, pp. 3677–3688, May2012.

469. Gilberto Reynoso-Meza, Javier Sanchis, Xavier Blasco and Juan M. Herrero, “Multiobjective evolutionary algorithmsfor multivariable PI controller design”, Expert Systems with Applications, Vol. 39, No. 9, pp. 7895–7907, July 2012.

470. Youcef Bouchebaba, Ali-Erdem Ozcan, Pierre Paulin and Gabriela Nicolescu, “MpAssign: a framework for solving themany-core platform mapping problem”, Software–Practice & Experience, Vol. 42, No. 7, pp. 891–915, July 2012.

471. Yakoub Bazi, Naif Alajlan and Farid Melgani, “Improved Estimation of Water Chlorophyll Concentration With Semisu-pervised Gaussian Process Regression”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 50, No. 7, pp.2733–2743, Part 2, July 2012.

472. Gustavo Olague and Leonardo Trujillo, “Interest point detection through multiobjective genetic programming”, AppliedSoft Computing, Vol. 12, No. 8, pp. 2566–2582, August 2012.

473. Ying Liu, Melody Kiang and Michael Brusco, “A unified framework for market segmentation and its applications”,Expert Systems with Applications, Vol. 39, No. 11, pp. 10292–10302, September 1, 2012.

474. K. Metaxiotis and K. Liagkouras, “Multiobjective Evolutionary Algorithms for Portfolio Management: A comprehensiveliterature review”, Expert Systems with Applications, Vol. 39, No. 14, pp. 11685–11698, October 15, 2012.

475. Federico Divina, Beatriz Pontes, Raul Giraldez and Jesus S. Aguilar-Ruiz, “An effective measure for assessing the qualityof biclusters”, Computers in Biology and Medicine, Vol. 42, No. 2, pp. 245–256, February 2012.

476. Massimo Vecchio, Roberto Lopez-Valcarce and Francesco Marcelloni, “A two-objective evolutionary approach based ontopological constraints for node localization in wireless sensor networks”, Applied Soft Computing, Vol. 12, No. 7, pp.1891–1901, July 2012.

477. Krzysztof Trawinski, Oscar Cordon and Arnaud Quirin, “A Study on the Use of Multiobjective Genetic Algorithmsfor Classifier Selection in FURIA-based Fuzzy Multiclassifiers”, International Journal of Computational IntelligenceSystems, Vol. 5, No. 2, pp. 231–253, April 2012.

478. El-Ghazali Talbi, Matthieu Basseur, Antonio J. Nebro and Enrique Alba, “Multi-objective optimization using meta-heuristics: non-standard algorithms”, International Transactions in Operational Research, Vol. 19, Nos. 1-2, pp. 283–305, January-March 2012.

479. Helon Vicente Hultmann Ayala and Leandro dos Santos Coelho, “Tuning of PID controller based on a multiobjectivegenetic algorithm applied to a robotic manipulator”, Expert Systems with Applications, Vol. 39, No. 10, pp. 8968–8974,August 2012.

480. Dedi Liu, Shenglian Guo, Xiaohong Chen, Quanxi Shao, Qihua Ran, Xingyuan Song and Zhaoli Wang, “A macro-evolutionary multi-objective immune algorithm with application to optimal allocation of water resources in DongjiangRiver basins, South China”, Stochastic Environmental Research and Risk Assessment, Vol. 26, No. 4, pp. 497–507, May2012.

481. Yong Zhang, Dun-Wei Gong and Zhonghai Ding, “A bare-bones multi-objective particle swarm optimization algorithmfor environmental/economic dispatch”, Information Sciences, Vol. 192, pp. 213–227, June 1, 2012.

482. A. Weber, S. Fasoulas and K. Wolf, “Conceptual interplanetary space mission design using multi-objective evolutionaryoptimization and design grammars”, Proceedings of the Institution of Mechanical Engineers Part G–Journal of AerospaceEngineering, Vol. 225, No. G11, pp. 1253–1261, November 2011.

483. I.G.P. Asto Buditjahjanto and Hajime Miyauchi, “An Intelligent Decision Support Based on a Subtractive Clustering andFuzzy Inference System for Multiobjective Optimization Problem in Serious Game”, International Journal of InformationTechnology & Decision Making, Vol. 10, No. 5, pp. 793–810, September 2011.

484. A. Kaveh and K. Laknejadi, “A Hybrid Multi-Objective Optimization and Decision Making Procedure for OptimalDesign of Truss Structures”, Iranian Journal of Science and Technology–Transactions of Civil Engineering, Vol. 35, No.C2, pp. 137–154, August 2011.

485. Juan J. Durillo and Antonio J. Nebro, “jMetal: A Java framework for multi-objective optimization”, Advances inEngineering Software, Vol. 42, No. 10, pp. 760–771, October 2011.

486. Reza Akbari and Koorush Ziarati, “Multi-objective Bee Swarm Optimization”, International Journal of InnovativeComputing Information and Control, Vol. 8, No. 1B, pp. 715–726, January 2012.

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487. Ali Kaveh, Karim Laknejadi and Babak Alinejad, “Performance-based multi-objective optimization of large steel struc-tures”, Acta Mechanica, Vol. 223, No. 2, pp. 355–369, February 2012.

488. Khaled Badran and Peter Rockett, “Multi-class pattern classification using single, multi-dimensional feature-space featureextraction evolved by multi-objective genetic programming and its application to network intrusion detection”, GeneticProgramming and Evolvable Machines, Vol. 13, No. 1, pp. 33–63, March 2012.

489. B. Naujoks, H. Trautmann, S. Wessing and C. Weihs, “Advanced concepts for multi-objective evolutionary optimizationin aircraft industry”, Proceedings of the Institution of Mechanical Engineers Part G–Journal of Aerospace Engineering,Vol. 225, No. G10, pp. 1081–1096, October 2011.

490. Chiu-Hung Chen, Tung-Kuan Liu, I-Ming Huang and Jyh-Horng Chou, “Multiobjective Synthesis of Six-bar MechanismsUnder Manufacturing and Collision-free Constraints”, IEEE Computational Intelligence Magazine, Vol. 7, No. 1, pp.36–48, February 2012.

491. Arnaud Zinflou, Caroline Gagne and Marc Gravel, “GISMOO: A new hybrid genetic/immune strategy for multiple-objective optimization”, Computers & Operations Research, Vol. 39, No. 9, pp. 1951–1968, September 2012.

492. Khairy Elsayed and Chris Lacor, “Modeling and Pareto optimization of gas cyclone separator performance using RBFtype artificial neural networks and genetic algorithms”, Poweder Technology, Vol. 217, pp. 84–99, February 2012.

493. Yavuz Cengiz and Eray Konar, “Pareto-optimal synthesis of microwave amplifier to design the noise-constrained gainvalue”, Microwave and Optical Technology Letters, Vol. 54, No. 4, pp. 1079–1084, April 2012.

494. T. Gomez, M. Hernandez, J. Molina, M.A. Leon, E. Aldana and R. Caballero, “A multiobjective model for forestplanning with adjacency constraints”, Annals of Operations Research, Vol. 190, No. 1, pp. 75–92, October 2011.

495. C.A. Garcia Montoya and S. Mendoza Toro, “Implementation of an evolutionary algorithm in planning investment in apower distribution system”, Revista Ingenierıa e Investigacion, Vol. 31, Supplement: 2, pp. 118–124, 2011.

496. Sunith Bandaru and Kalyanmoy Deb, “Towards automating the discovery of certain innovative design principles througha clustering-based optimization technique”, Engineering Optimization, Vol. 43, No. 9, pp. 911–941, 2011.

497. Shuo Xu, Ze Ji, Duc Truong Pham and Fan Yu, “Binary Bees Algorithm - bioinspiration from the foraging mechanismof honeybees to optimize a multiobjective multidimensional assignment problem”, Engineering Optimization, Vol. 43,No. 11, pp. 1141–1159, 2011.

498. Ben G. Small, Barry W. McColl, Richard Allmendinger, Jurgen Pahle, Gloria Lopez-Castejon, Nancy J. Rothwell, JoshuaKnowles, Pedro Mendes, David Brough and Doublas B. Kell, “Efficient discovery of anti-inflammatory small-moleculecombinations using evolutionary computing”, Nature Chemical Biology, Vol. 7, No. 12, pp. 902–908, December 2011.

499. Manojkumar Ramteke and Rajagopalan Srinivasan, “Novel genetic algorithm for short-term scheduling of sequencedependent changeovers in multiproduct polymer plants”, Computers & Chemical Engineering, Vol. 35, No. 12, pp.2945–2959, December 14, 2011.

500. Yun-Geun Lee, Bob Mckay, Kang-Il Kim, Dong-Kyun Kim and Nguyen Xuan Hoai, “Investigating vesicular selection Aselection operator in in vitro evolution”, Applied Soft Computing, Vol. 11, No. 8, pp. 5528–5550, December 2011.

501. Guillermo Molina, Francisco Luna, Antonio J. Nebro and Enrique Alba, “An efficient local improvement operator forthe multi-objective wireless sensor network deployment problem”, Engineering Optimization, Vol. 43, No. 10, pp.1115–1139, 2011.

502. W.L. Wang, X.J. Yang, G.X. Xu and Y. Huang, “Multi-objective design optimization of the complete valve systemin an adjustable linear hydraulic damper”, Proceedings of the Institution of Mechanical Engineers Part C–Journal ofMechanical Engineering Science, Vol. 225, No. C3, pp. 679–699, 2011.

503. Jose L. Bernal-Agustin and Rodolfo Dufo-Lopez, “Simulation and optimization of stand-alone hybrid renewable energysystems”, Renewable & Sustainable Energy Reviews, Vol. 13, No. 8, pp. 2111–2118, October 2009.

504. Kwang Mong Sim and Bo An, “Evolving Best-Response Strategies for Market-Driven Agents Using Aggregative FitnessGA”, IEEE Transactions on Systems, Man, and Cybernetics Part C–Applications and Reviews, Vol. 39, No. 3, pp.284–298, May 2009.

505. Md. Rafiul Hassan, Baikunth Nath, Michael Kirley and Joarde Kamruzzaman, “A hybrid of multiobjective EvolutionaryAlgorithm and HMM-Fuzzy model for time series prediction”, Neurocomputing, Vol. 81, pp. 1–11, April 1, 2012.

506. Kent McClymont and Ed Keedwell, “Deductive Sort and Climbing Sort: New Methods for Non-Dominated Sorting”,Evolutionary Computation, Vol. 20, No. 1, pp. 1–26, Spring 2012.

507. Teodor Marcu, Birgit Koppen-Seliger and Reinhard Stucher, “Design of fault detection for a hydraulic looper usingdynamic neural networks”, Control Engineering Practice, Vol. 16, No. 2, pp. 192–213, February 2008.

508. Christian Gagne and Marc Parizeau, “Coevolution of nearest neighbor classifiers”, International Journal of PatternRecognition and Artificial Intelligence, Vol. 21, No. 5, pp. 921–946, August 2007.

509. B.Y. Qu and P.N. Suganthan, “Constrained multi-objective optimization algorithm with an ensemble of constrainthandling methods”, Engineering Optimization, Vol. 43, No. 4, pp. 403–416, 2011.

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510. Manojkumar Ramteke and Santosh K. Gupta, “Kinetic Modeling and Reactor Simulation and Optimization of Industri-ally Important Polymerization Processes: a Perspective”, International Journal of Chemical Reactor Engineering, Vol.9, Article Number: R1, 2011.

511. Arnaud Liefooghe, Laetitia Jourdan and El-Ghazali Talbi, “A software framework based on a conceptual unified modelfor evolutionary multiobjective optimization: ParadisEO-MOEO”, European Journal of Operational Research, Vol. 209,No. 2, pp. 104–112, March 1, 2011.

512. Massimiliano Manfren, Paola Caputo and Gaia Costa, “Paradigm shift in urban energy systems through distributedgeneration: Methods and models”, Applied Energy, Vol. 88, No. 4, pp. 1032–1048, April 2011.

513. P.M. Reed and J.B. Kollat, “Save now, pay later? Multi-period many-objective groundwater monitoring design givensystematic model errors and uncertainty”, Advances in Water Resources, Vol. 35, pp. 55–68, January 2012.

514. C.W. Bong and M. Rajeswari, “Multiobjective clustering with metaheuristic: current trends and methods in imagesegmentation”, IET Image Processing, Vol. 6, No. 1, pp. 1–10, February 2012.

515. Douglas A.G. Vieira, Ricardo H.C. Takahashi and Rodney R. Saldanha, “Multicriteria optimization with a multiobjectivegolden section line search”, Mathematical Programming, Vol. 131, Nos. 1-2, pp. 131–161, February 2012.

516. H. Kordabadi and A. Jahanmiri, “A pseudo-dynamic optimization of a dual-stage methanol synthesis reactor in the faceof catalyst deactivation”, Chemical Engineering and Processing, Vol. 46, No. 12, pp. 1299–1309, December 2007.

517. David Daum and Nicolas Morel, “Assessing the saving potential of blind controller via multi-objective optimization”,Building Simulation, Vol. 2, No. 3, pp. 175–185, September 2009.

518. Wei-Mei Chen, Hsien-Kuei Hwang and Tsung-Hsi Tsai, “Maxima-finding algorithms for multidimensional samples: Atwo-phase approach”, Computational Geometry–Theory and Applications, Vol. 45, Nos. 1-2, pp. 33–53, January-February 2012.

519. Lina Perelman, Avi Ostfeld and Elad Salomons, “Cross Entropy multiobjective optimization for water distributionsystems design”, Water Resources Research, Vol. 44, No. 9, Article Number: W09413, September 10, 2008.

520. Michael A. Trick and Hakan Yildiz, “Locally Optimized Crossover for the Traveling Umpire Problem”, European Journalof Operational Research, Vol. 216, No. 2, pp. 286–292, January 16, 2012.

521. Diego P. Pinto-Roa, Benjamin Baran and Carlos A. Brizuela, “Routing and wavelength converter allocation in WDMnetworks: a multi-objective evolutionary optimization approach”, Photonic Network Communications, Vol. 22, No. 1,pp. 23–45, August 2011.

522. Marc Holze and Norbert Ritter, “System models for goal-driven self-management in autonomic databases”, Data &Knowledge Engineering, Vol. 70, No. 8, pp. 685–701, August 2011.

523. Karsten Hentsch and Peter Kochel, “Job scheduling with forbidden setups and two objectives using genetic algorithmsand penalties”, Central European Journal of Operations Research, Vol. 19, No. 3, pp. 285–298, September 2011.

524. Renata Furtuna, Silvia Curteanu and Carmen Racles, “NSGA-II-RJG applied to multi-objective optimization of poly-meric nanoparticles synthesis with silicone surfactants”, Central European Journal of Chemistry, Vol. 9, No. 6, pp.1080–1095, December 2011.

525. Ronghua Shang, Licheng Jiao, Fang Liu and Wenping Ma, “A Novel Immune Clonal Algorithm for MO Problems”,IEEE Transactions on Evolutionary Computation, Vol. 16, No. 1, pp. 35–50, February 2012.

526. K.C. Tan, Q. Yu and J.H. Ang, “A dual-objective evolutionary algorithm for rules extraction in data mining”, Compu-tational Optimization and Applications, Vol. 34, No. 2, pp. 273–294, June 2006.

527. J.B. Kollat, P.M. Reed and J.R. Kasprzyk, “A new epsilon-dominance hierarchical Bayesian optimization algorithm forlarge multiobjective monitoring network design problems”, Advances in Water Resources, Vol. 31, No. 5, pp. 828–845,May 2008.

528. Mohammed Shalaby and Kazuhiro Saitou, “Design for Disassembly With High-Stiffness Heat-Reversible Locator-SnapSystems”, Journal of Mechanical Design, Vol. 130, No. 12, Article Number: 121701, December 2008.

529. B. Descamps, R. Filomeno Coelho, L. Ney and Ph. Bouillard, “Multicriteria optimization of lightweight bridge structureswith a constrained force density method”. Computers & Structures, Vol. 89, Nos. 3-4, pp. 277–284, February 2011.

530. I-Tung Yang and Jui-Sheng Chou, “Multiobjective optimization for manpower assignment in consulting engineeringfirms”, Applied Soft Computing, Vol. 11, No. 1, pp. 1183–1190, January 2011.

531. Hesham Kamel, Ramin Sedaghati and Mamoun Medraj, “Crashworthiness improvement of a pickup truck’s chassis frameusing the Pareto-Front and genetic algorithm”, International Journal of Heavy Vehicle Systems, Vol. 18, No. 1, pp.83–103, 2011.

532. Wahabou Abdou, Adrien Henriet, Christelle Bloch, Dominique Dhoutaut, Damien Charlet and Francois Spies, “Usingan evolutionary algorithm to optimize the broadcasting methods in mobile ad hoc networks”, Journal of Network andComputer Applications, Vol. 34, No. 6, pp. 1794–1804, November 2011.

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533. Yongtai Huang and Lei Liu, “Multiobjective Water Quality Model Calibration Using a Hybrid Genetic Algorithm andNeural Network-Based Approach”, Journal of Environmental Engineering–ASCE, Vol. 136, pp. 1020–1031, October2010.

534. Bruno Urli and Francois Terrien, “Project portfolio selection model, a realistic approach”, International Transactions inOperational Research, Vol. 17, No. 6, pp. 809–826, November 2010.

535. Roger M. Jarvis, William Rowe, Nicola R. Yaffe, Richard O’Connor, Joshua D. Knowles, Ewan W. Blanch and RoystonGoodacre, “Multiobjective evolutionary optimisation for surface-enhanced Raman scattering”, Analytical and Bioana-lytical Chemistry, Vol. 397, No. 5, pp. 1893–1901, July 2010.

536. David Daum and Nicolas Morel, “Assessing the total energy impact of manual and optimized blind control in combinationwith different lighting schedules in a building simulation environment”, Journal of Building Performance Simulation,Vol. 3, No. 1, pp. 1–16, 2010.

537. Minqiang Li, Dan Lin and Shouyang Wang, “Solving a type of biobjective bilevel programming problem using NSGA-II”,k Computers & Mathematics with Applications, Vol. 59, No. 2, pp. 706–715, January 2010.

538. Zhe Xu and Susan Lu, “Multi-objective optimization of sensor array using genetic algorithm”, Sensors and ActuatorsB-Chemical, Vol. 160, No. 1, pp. 278–286, December 15, 2011.

539. Karim Hamza and Kazuhiro Saitou, “A Co-Evolutionary Approach for Design Optimization via Ensembles of SurrogatesWith Application to Vehicle Crashworthiness”, Journal of Mechanical Design, Vol. 134, No. 1, Article Number: 011001,January 2012.

540. K. Rodriguez-Vazquez, M.L. Arganis-Juarez, C. Cruickshank-Villanueva and R. Dominguez-Mora, “Rainfall-runoff mod-elling using genetic programming”, Journal of Hydroinformatics, Vol. 14, No. 1, pp. 108–121, January 2012.

541. Monica Carvalho, Miguel A. Lozano and Luis M. Serra, “Multicriteria synthesis of trigeneration systems consideringeconomic and environmental aspects”, Applied Energy, Vol. 91, No. 1, pp. 245–254, March 2012.

542. Peter A. N. Bosman, “On Gradients and Hybrid Evolutionary Algorithms for Real-Valued Multiobjective Optimization”,IEEE Transactions on Evolutionary Computation, Vol. 16, No. 1, pp. 51–69, February 2012.

543. Rocio L. Cecchini, Ignacio Ponzoni and Jessica A. Carballido, “Multi-objective evolutionary approaches for intelligentdesign of sensor networks in the petrochemical industry”, Expert Systems with Applications, Vol. 39, No. 3, pp. 2643–2649, February 15, 2012.

544. Wenping Zou, Yunlong Zhu, Hanning Chen and Beiwei Zhang, “Solving Multiobjective Optimization Problems UsingArtificial Bee Colony Algorithm”, Discrete Dynamics in Nature and Society, Article Number: 569784, 2011.

545. Eduardo Fernandez Gonzalez, Edy Lopez Cervantes, Jorge Navarro Castillo and Ines Vega Lopez, “Application of Multi-Objective Metaheuristics to Public Portfolio Selection Through Multidimensional Modelling of Social Return”, Gestiony Politica Publica, Vol. 20, No. 2, pp. 381–432, 2011.

546. Shuang Wei and Henry Leung, “A Novel Ranking Method Based on Subjective Probability Theory for EvolutionaryMultiobjective Optimization”, Mathematical Problems in Engineering, Article Number: 695087, 2011.

547. Joaquin Izquierdo, Idel Montalvo, Rafael Perez-Garcia and Agustin Matias, “On the Complexities of the Design of WaterDistribution Networks”, Mathematical Problems in Engineering, Vol. Article Number: 947961, 2012.

548. Karthik Sindhya, Kalyanmoy Deb and Kaisa Miettinen, “Improving convergence of evolutionary multi-objective op-timization with local search: a concurrent-hybrid algorithm”, Natural Computing, Vol. 10, No. 4, pp. 1407–1430,December 2011.

549. Dan Zhang and Zhen Gao, “Hybrid head mechanism of the groundhog-like mine rescue robot”, Robotics and Computer-Integrated Manufacturing, Vol. 27, No. 2, pp. 460–470, April 2011.

550. Weihong Li, Lijuan Liu and Weiguo Gong, “Multi-objective uniform design as a SVM model selection tool for facerecognition”, Expert Systems with Applications, Vol. 38, No. 6, pp. 6689–6695, June 2011.

551. Gustavo Olague and Leonardo Trujillo, “Evolutionary-computer-assisted design of image operators that detect interestpoints using genetic programming”, Image and Vision Computing, Vol. 29, No. 7, pp. 484–498, June 2011.

552. Kejing Li and Xiaobing Zhang, “Multi-Objective Optimization of Interior Ballistic Performance Using NSGA-II”, Pro-pellants Explosives Pyrotechnics, Vol. 36, No. 3, pp. 282–290, June 2011.

553. Everardo Gutierrez and Carlos Brizuela, “An Enhanced MOGWW for the bi-objective Quadratic Assignment Problem”,International Journal of Computational Intelligence Systems, Vol. 4, No. 4, pp. 530–549, June-August 2011.

554. Shih-Pin Chen and Ming-Jiun Tsai, “Time-cost trade-off analysis of project networks in fuzzy environments”, EuropeanJournal of Operational Research, Vol. 212, No. 2, pp. 386–397, July 16, 2011.

555. Kishalay Mitra and Sushanta Majumder, “Successive approximate model based multi-objective optimization for anindustrial straight grate iron ore induration process using evolutionary algorithm”, Chemical Engineering Science, Vol.66, No. 15, pp. 3471–3481, August 1, 2011.

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556. E. David Ford and Maureen C. Kennedy, “Assessment of uncertainty in functional-structural plant models”, Annals ofBotany, Vol. 108, No. 6, pp. 1043–1053, October 2011.

557. Antonio L. Marquez, Raul Banos, Consolacion Gil, Maria G. Montoya, Francisco Manzano-Agugliaro and Francisco G.Montoya, “Multi-objective crop planning using pareto-based evolutionary algorithms”, Agricultural Economics, Vol. 42,No. 6, pp. 649–656, November 2011.

558. Oscar Daniel Chuk and Benjamin R. Kuchen, “Supervisory control of flotation columns using multi-objective optimiza-tion”, Minerals Engineering, Vol. 24, No. 14, pp. 1545–1555, November 2011.

559. H. Komoto, T. Tomiyama, S. Silvester, and H. Brezet, “Analyzing supply chain robustness for OEMs from a life cycleperspective using life cycle simulation”, International Journal of Production Economics, Vol. 134, No. 2, pp. 447–457,December 2011.

560. Rajan Filomeno Coelho and Philippe Bouillard, “Multi-Objective Reliability-Based Optimization with Stochastic Meta-models”, Evolutionary Computation, Vol. 19, No. 4, pp. 525–560, Winter 2011.

561. Rafael Alcala, Yusuke Nojima, Francisco Herrera and Hisao Ishibuchi, “Multiobjective genetic fuzzy rule selection ofsingle granularity-based fuzzy classification rules and its interaction with the lateral tuning of membership functions”,Soft Computing, Vol. 15, No. 12, pp. 2303–2318, December 2011.

562. Leonardo Trujillo, Gustavo Olague, Evelyne Lutton, Francisco Fernandez de Vega, Leon Dozal and Eddie Clemente,“Speciation in Behavioral Space for Evolutionary Robotics”, Journal of Intelligent & Robotic Systems, Vol. 64, Nos.3-4, pp. 323–351, December 2011.

563. Rajeev Kumar and Nilanjan Banerjee, “Multiobjective network topology design”, Applied Soft Computing, Vol. 11, No.8, pp. 5120–5128, December 2011.

564. Ignacy Kaliszewski, J. Miroforidis and Dmitry Podkopaev, “Interactive Multiple Criteria Decision Making based on pref-erence driven Evolutionary Multiobjective Optimization with controllable accuracy”, European Journal of OperationalResearch, Vol. 216, No. 1, pp. 188–199, January 1, 2012.

565. S. Afshin Mansouri, David Gallear and Mohammad H. Askariazad, “Decision support for build-to-order supply chainmanagement through multiobjective optimization”, International Journal of Production Economics, Vol. 135, No. 1,pp. 24–36, January 2012.

566. Sultan Noman Qasem and Siti Mariyam Shamsuddin, “Memetic Elitist Pareto Differential Evolution algorithm basedRadial Basis Function Networks for classification problems”, Applied Soft Computing, Vol. 11, No. 8, pp. 5565–5581,December 2011.

567. Hisao Ishibuchi, Yusuke Nakashima and Yusuke Nojima, “Performance evaluation of evolutionary multiobjective opti-mization algorithms for multiobjective fuzzy genetics-based machine learning”, Soft Computing, Vol. 15, No. 12, pp.2415–2434, December 2011.

568. Ke Li, Sam Kwong, Jingjing Cao, Miqing Li, Jinhua Zheng and Ruimin Shen, “Achieving balance between proximityand diversity in multi-objective evolutionary algorithm”, Information Sciences, Vol. 182, No. 1, pp. 220–242, January1, 2012.

569. Andre B. de Carvalho and Aurora Pozo, “Measuring the convergence and diversity of CDAS Multi-Objective ParticleSwarm Optimization Algorithms: A study of many-objective problems”, Neurocomputing, Vol. 75, No. 1, pp. 43–51,January 1, 2012.

570. H. Li and D. Landa-Silva, “An Adaptive Evolutionary Multi-Objective Approach Based on Simulated Annealing”,Evolutionary Computation, Vol. 19, No. 4, pp. 561–595, Winter 2011.

571. Thomas Tometzki and Sebastian Engell, “Risk conscious solution of planning problems under uncertainty by hybridmulti-objective evolutionary algorithms”, Computers & Chemical Engineering, Vol. 35, No. 11, pp. 2521–2539, Novem-ber 15, 2011.

572. Hans-Friedrich Kohn, “A review of multiobjective programming and its application in quantitative psychology”, Journalof Mathematical Psychology, Vol. 55, No. 5, pp. 386–396, October 2011.

573. Sai Ho Yeung and Kim Fung, “Multiobjective Optimization”, IEEE Microwave Magazine, Vol. 12, No. 6, pp. 120–133,October 2011.

574. A. Kaveh and K. Laknejadi, “A novel hybrid charge system search and particle swarm optimization method for multi-objective optimization”, Expert Systems with Applications, Vol. 38, No. 12, pp. 15475–15488, November-December2011.

575. K.P. Anagnostopoulos and G. Mamanis, “The mean-variance cardinality constrained portfolio optimization problem:An experimental evaluation of five multiobjective evolutionary algorithms”, Expert Systems with Applications, Vol. 38,No. 11, pp. 14208–14217, October 2011.

576. Karthik Sindhya, Sauli Ruuska, Tomi Haanpaa and Kaisa Miettinen, “A new hybrid mutation operator for multiobjectiveoptimization with differential evolution”, Soft Computing, Vol. 15, No. 10, pp. 2041–2055, October 2011.

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577. Yi Chen, Yong Ma, Zheng Lu, Lixia Qiu and Jin He, “Terahertz spectroscopic uncertainty analysis for explosive mixturecomponents determination using multi-objective micro-genetic algorithm”, Advances in Engineering Software, Vol. 42,No. 9, pp. 649–659, September 2011.

578. Emiliano Carreno Jara, “Long memory time series forecasting by using genetic programming”, Genetic Programmingand Evolvable Machines, Vol. 12, No. 4, pp. 429–456, December 2011.

579. Zbigniew Sekulski, “Multi-objective optimization of high speed vehicle-passenger catamaran by genetic algorithm PartIII Analysis of the results”, Polish Maritime Research, Vol. 18, No. 4, pp. 3–13, 2011.

580. Zbigniew Sekulski, “Multi-objective optimization of high speed vehicle-passenger catamaran by genetic algorithm PartII Computational simulations”, Polish Maritime Research, Vol. 18, No. 3, pp. 3–30, 2011.

581. Zbigniew Sekulski, “Multi-objective topology and size optimization of high-speed vehicle-passenger catamaran structureby genetic algorithm”, Marine Structures, Vol. 23, No. 4, pp. 405–433, October 2010.

582. Yong Zhang, Dun-wei Gong and Zhong-hai Ding, “Handling multi-objective optimization problems with a multi-swarmcooperative particle swarm optimizer”, Expert Systems with Applications, Vol. 38, No. 11, pp. 13933–13941, October2011.

583. Hisao Ishibuchi, Yuji Sakane, Noritaka Tsukamoto and Yusuke Nojima, “Implementation of cellular genetic algorithmswith two neighborhood structures for single-objective and multi-objective optimization”, Soft Computing, Vol. 15, No.9, pp. 1749–1767, September 2011.

584. Rodolfo Dufo-Lopez, Jose L. Bernal-Agustin, Jose M. Yusta-Loyo, Jose A. Dominguez-Navarro, Ignacio J. Ramirez-Rosado, Juan Lujano and Ismael Aso, “Multi-objective optimization minimizing cost and life cycle emissions of stand-alone PV-wind-diesel systems with batteries storage”, Applied Energy, Vol. 88, No. 11, pp. 4033–4041, November2011.

585. I. Alberto and P.M. Mateo, “A crossover operator that uses Pareto optimality in its definition”, TOP, Vol. 19, No. 1,pp. 67–92, July 2011.

586. Manuel Chica, Oscar Cordon and Sergio Damas, “An advanced multiobjective genetic algorithm design for the time andspace assembly line balancing problem”, Computers & Industrial Engineering, Vol. 61, No. 1, pp. 103–117, August2011.

587. David R. White, Andrea Arcuri and John A. Clark, “Evolutionary Improvement of Programs”, IEEE Transactions onEvolutionary Computation, Vol. 15, No. 4, pp. 515–538, August 2011.

588. Slim Bechikh, Lamjed Ben Said and Khaled Ghedira, “Searching for knee regions of the Pareto front using mobilereference points”, Soft Computing, Vol. 15, No. 9, pp. 1807–1823, 2011.

589. Alvaro Luis Bustamante, Jose M. Molina Lopez and Miguel A. Patricio, “MIJ2K Optimization using evolutionarymultiobjective optimization algorithms”, Expert Systems with Applications, Vol. 38, No. 9, pp. 10999–11010, September2011.

590. Renata Furtuna, Silvia Curteanu and Florin Leon, “An elitist non-dominated sorting genetic algorithm enhanced with aneural network applied to the multi-objective optimization of a polysiloxane synthesis process”, Engineering Applicationsof Artificial Intelligence, Vol. 24, No. 5, pp. 772–785, August 2011.

591. Debanga Nandan Mondal, Kadambini Sarangi, Frank Pettersson, Prodip Kumar Sen, Henrik Saxen and NirupamChakraborti, “Cu-Zn separation by supported liquid membrane analyzed through Multi-objective Genetic Algorithms”,Hydrometallurgy, Vol. 107, Nos. 3-4, pp. 112–123, May 2011.

592. Oscar Cordon, “A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: De-signing interpretable genetic fuzzy systems”, International Journal of Approximate Reasoning, Vol. 52, No. 6, pp.894–913, September 2011.

593. H. Moradi, M. Zandieh and Iraj Mahdavi, “Non-dominated ranked genetic algorithm for a multi-objective mixed-modelassembly line sequencing problem”, International Journal of Production Research, Vol. 49, No. 12, pp. 3479–3499, 2011.

594. Wei-Chang Yeh and Mei-Chi Chuang, “Using multi-objective genetic algorithm for partner selection in green supplychain problems”, Expert Systems with Applications, Vol. 38, No. 4, pp. 4244–4253, April 2011.

595. Reza Ghaemi, Nasir bin Sulaiman, Hamidah Ibrahim and Norwati Mustapha, “A review: accuracy optimization inclustering ensembles using genetic algorithms”, Artificial Intelligence Review, Vol. 35, No. 4, pp. 287–318, April 2011.

596. Shafaq B. Chaudhry, Victor C. Hung, Ratan K. Guha and Kenneth O. Stanley, “Pareto-based evolutionary computationalapproach for wireless sensor placement”, Engineering Applications of Artificial Intelligence, Vol. 24, No. 3, pp. 409–425,April 2011.

597. H. Safikhani, M.A. Akhavan-Behabadi, N. Nariman-Zadeh and M.J. Mahmood Abadi, “Modeling and multi-objectiveoptimization of square cyclones using CFD and neural networks”, Chemical Engineering Research & Design, Vol. 89,No. 3A, pp. 301–309, March 2011.

598. M.Sh. Levin and M.V. Petukhov, “Connection of Users with a Telecommunications Network: Multicriteria AssignmentProblem”, Journal of Communications Technology and Electronics, Vol. 55, No. 12, pp. 1532–1541, December 2010.

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599. P. Ghobadi, M. Yahyaei and S. Banisi, “Optimization of the performance of flotation circuits using a genetic algorithmoriented by process-based rules”, International Journal of Mineral Processing, Vol. 98, Nos. 3-4, pp. 174–181, March 9,2011.

600. Peter Vamplew, Richard Dazeley, Adam Berry, Rustam Issabekov and Evan Dekker, “Empirical evaluation methods formultiobjective reinforcement learning algorithms”, Machine Learning, Vol. 84, Nos. 1-2, pp. 51–80, July 2011.

601. Luis A. Moncayo-Martinez and David Z. Zhang, “Multi-objective ant colony optimisation: A meta-heuristic approachto supply chain design”, International Journal of Production Economics, Vol. 131, No. 1, pp. 407–420, May 2011.

602. M.P. Cuellar, S. Capel-Cuevas, M.C. Pegalajar, I. de Orbe-Paya and L.F. Capitan-Vallvey, “Minimization of sensingelements for full-range optical pH device formulation”, New Journal of Chemistry, Vol. 35, No. 5, pp. 1042–1053, 2011.

603. B. Sankararao and Chang Kyoo Yoo, “Development of a Robust Multiobjective Simulated Annealing Algorithm forSolving Multiobjective Optimization Problems”, Industrial & Engineering Chemistry Research, Vol. 50, No. 11, pp.6728–6742, June 1, 2011.

604. Pankaj Rajak, Ujjal Tewary, Sumitesh Das, Baidurya Bhattacharya and Nirupam Chakraborti, “Phases in Zn-coatedFe analyzed through an evolutionary meta-model and multi-objective Genetic Algorithms”, Computational MaterialsScience, Vol. 50, No. 8, pp. 2502–2516, June 2011.

605. Itishree Mohanty, Debashish Bhattacharjee and Shubhabrata Datta, “Designing cold rolled IF steel sheets with optimizedtensile properties using ANN and GA”, Computational Materials Science, Vol. 50, No. 8, pp. 2331–2337, June 2011.

606. Marianne Boix, Ludovic Montastruc, Luc Pibouleau, Catherine Azzaro-Pantel and Serge Domenech, “A multiobjectiveoptimization framework for multicontaminant industrial water network design”, Journal of Environmental Management,Vol. 92, No. 7, pp. 1802–1808, July 2011.

607. Zhanpeng Jin and Allen C. Cheng, “SubsetTrio: An Evolutionary, Geometric, and Statistical Benchmark SubsettingFramework”, ACM Transactions on Modeling and Computer Simulation, Vol. 21, No. 3, Article Number: 21, March2011.

608. Chien-Ho Ko and Shu-Fan Wang, “Precast production scheduling using multi-objective genetic algorithms”, ExpertSystems with Applications, Vol. 38, No. 7, pp. 8293–8302, July 2011.

609. Darrell F. Lochtefeld and Frank W. Ciarallo, “Helper-objective optimization strategies for the Job-Shop SchedulingProblem”, Applied Soft Computing, Vol. 11, No. 6, pp. 4161–4174, September 2011.

610. Markus Hartikainen, Kaisa Miettinen and Margaret M. Wiecek, “Constructing a Pareto front approximation for decisionmaking”, Mathematical Methods of Operations Research, Vol. 73, No. 2, pp. 209–234, April 2011.

611. Ata Allah Taleizadeh, Farnaz Barzinpour and Hui-Ming Wee, “Meta-heuristic algorithms for solving a fuzzy single-periodproblem”, Mathematical and Computer Modelling, Vol. 54, Nos. 5-6, pp. 1273–1285, September 2011.

612. Jiaquan Gao and Jun Wang, “A hybrid quantum-inspired immune algorithm for multiobjective optimization”, AppliedMathematics and Computation, Vol. 217, No. 9, pp. 4754–4770, January 1, 2011.

613. Y.P. Ju and C.H. Zhang, “Multi-point and multi-objective optimization design method for industrial axial compressorcascades”, Proceedings of the Institution of Mechanical Engineers Part C–Journal of Mechanical Engineering Science,Vol. 225, No. C6, pp. 1481–1493, 2011.

614. A. Kundu and P.K. Dan, “The Scope of Genetic Algorithms in Dealing with Facility Layout Problems”, South AfricanJournal of Industrial Engineering, Vol. 21, No. 2, pp. 39–49, November 2010.

615. Ernesto Benini, Rita Ponza and Andrea Massaro, “High-Lift Multi-Element Airfoil Shape and Setting OptimizationUsing Multi-Objective Evolutionary Algorithms”, Journal of Aircraft, Vol. 48, No. 2, pp. 683–696, March-April 2011.

616. Kishalay Mitra, “Handling Uncertainty in Kinetic Parameters in Optimal Operation of a Polymerization Reactor”,Materials and Manufacturing Processes, Vol. 26, No. 3, pp. 446–454, 2011.

617. Yu Wang, Bin Li and Yunbi Chen, “Digital IIR filter design using multi-objective optimization evolutionary algorithm”,Applied Soft Computing, Vol. 11, No. 2, pp. 1851–1857, March 2011.

618. Yi Sun, Chaoyong Zhang, Liang Gao and Xiaojuan Wang, “Multi-objective optimization algorithms for flow shopscheduling problem: a review and prospects”, International Journal of Advanced Manufacturing Technology, Vol. 55,Nos. 5-8, pp. 723–739, July 2011.

619. Ali R. Yildiz and Kazuhiro Saitou, “Topology Synthesis of Multicomponent Structural Assemblies in Continuum Do-mains”, Journal of Mechanical Design, Vol. 133, No. 1, Article Number: 011008, January 2011.

620. Yu Liang, XiaoQuan Cheng, ZhengNeng Li and JinWu Xiang, “Multi-objective robust airfoil optimization based onnon-uniform rational B-spline (NURBS) representation”, Science China-Technological Sciences, Vol. 53, No. 10, pp.2708–2717, October, 2010.

621. Satoshi Kitayama, Masao Arakawa and Koetsu Yamazaki, “Differential evolution as the global optimization techniqueand its application to structural optimization”, Applied Soft Computing, Vol. 11, No. 4, pp. 3792–3803, June 2011.

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622. R.P. Dionisio, G. Parca, C. Reis and A.L. Teixeira, “Operational parameter optimisation of MZI-SOA using multi-objective genetic algorithms”, Electronics Letters, Vol. 47, No. 9, pp. 561–562, April 28, 2011.

623. Gustavo C.M. Ferreira, S.P.N. Cani, M.J. Pontes and M.E.V. Segatto, “Optimization of Distributed Raman AmplifiersUsing a Hybrid Genetic Algorithm With Geometric Compensation Technique”, IEEE Photonics Journal, Vol. 3, No. 3,pp. 390–399, June 2011.

624. P. Rocca, G. Oliveri and A. Massa, “Differential Evolution as Applied to Electromagnetics”, IEEE Antennas andPropagation Magazine, Vol. 53, No. 1, pp. 38–49, February 2011.

625. Yu Liang, Xiao-quan Cheng, Zheng-neng Li and Jin-wu Xiang, “Robust Multi-Objective Wing Design Optimization viaCFD Approximation Model”, Engineering Applications of Computational Fluid Mechanics, Vol. 5, No. 2, pp. 286–300,June 2011.

626. Yann Cooren, Maurice Clerc and Patrick Siarry, “MO-TRIBES, an adaptive multiobjective particle swarm optimizationalgorithm”, Computational Optimization and Applications, Vol. 49, No. 2, pp. 379–400, June 2011.

627. Miltiadis Kotinis, “Implementing co-evolution and parallelization in a multi-objective particle swarm optimizer”, Engi-neering Optimization, Vol. 43, No. 6, pp. 635–656, June 2011.

628. Karthik Sindhya and Kaisa Miettinen, “New Perspective to Continuous Casting of Steel with a Hybrid EvolutionaryMultiobjective Algorithm”, Materials and Manufacturing Processes, Vol. 26, No. 3, pp. 481–492, 2011.

629. Rajan Filomeno Coelho, Jeremy Lebon and Philippe Bouillard, “Hierarchical stochastic metamodels based on movingleast squares and polynomial chaos expansion”, Structural and Multidisciplinary Optimization, Vol. 43, No. 5, pp.707–729, May 2011.

630. Debarati Kundu, Kaushik Suresh, Sayan Ghosh, Swagatam Das, B.K. Panigrahi and Sanjoy Das, “Multi-objectiveoptimization with artificial weed colonies”, Information Sciences, Vol. 181, No. 12, pp. 2441–2454, June 15, 2011.

631. Chin-Wei Bong and Mandava Rajeswari, “Multi-objective nature-inspired clustering and classification techniques forimage segmentation”, Applied Soft Computing, Vol. 11, No. 4, pp. 3271–3282, June 2011.

632. A. Castelletti, A.V. Lotov and R. Soncini-Sessa, “Visualization-based multi-objective improvement of environmentaldecision-making using linearization of response surfaces”, Environmental Modelling & Software, Vol. 25, No. 12, pp.1552–1564, December 2010.

633. Ruchit Shah and Patrick Reed, “Comparative analysis of multiobjective evolutionary algorithms for random and cor-related instances of multiobjective d-dimensional knapsack problems”, European Journal of Operational Research, Vol.211, No. 3, pp. 466–479, June 16, 2011.

634. Salem F. Adra and Peter J. Fleming, “Diversity Management in Evolutionary Many-Objective Optimization”, IEEETransactions on Evolutionary Computation, Vol. 15, No. 2, pp. 183–195, April 2011.

635. Burcin Cakir, Fulya Altiparmak and Berna Dengiz, “Multi-objective optimization of a stochastic assembly line balancing:A hybrid simulated annealing algorithm”, Computers & Industrial Engineering, Vol. 60, No. 3, pp. 376–384, April 2011.

636. Renan Cabrera, Ofer M. Shir, Rebing Wu and Herschel Rabitz, “Fidelity between unitary operators and the generationof robust gates against off-resonance perturbations”, Journal of Physics A–Mathematical and Theoretical, Vol. 44, No.9, Article Number 095302, March 4, 2011.

637. Nadia Nedjah, Marcus Vinicius Carvalho da Silva and Luiza de Macedo Mourelle, “Customized computer-aided applica-tion mapping on NoC infrastructure using multi-objective optimization”, Journal of Systems Architecture, Vol. 57, No.1, pp. 79–94, January 2011.

638. James Bekker and Chris Aldrich, “The cross-entropy method in multi-objective optimisation: An assessment”, EuropeanJournal of Operational Research, Vol. 211, No. 1, pp. 112–121, May 16, 2011.

639. Fatimah Sham Ismail, Rubiyah Yusof and Marzuki Khalid, “Self Organizing Multi-Objective Optimization Problem”,International Journal of Innovative Computing Information and Control, Vol. 7, No. 1, pp. 301–314, January 2011.

640. Zhong-Zhong Jiang, W.H. Ip, H.C.W. Lau and Zhi-Ping Fan, “Multi-objective optimization matching for one-shotmulti-attribute exchanges with quantity discounts in E-brokerage”, Expert Systems with Applications, Vol. 38, No. 4,pp. 4169–4180, April 2011.

641. K. Sivakumar, C. Balamurugan and S. Ramabalan, “Simultaneous optimal selection of design and manufacturing toler-ances with alternative manufacturing process selection”, Computer-Aided Design, Vol. 43, No. 2, pp. 207–218, February2011.

642. Prithwish Chakraborty, Swagatam Das, Gourab Ghosh Roy and Ajith Abraham, “On convergence of the multi-objectiveparticle swarm optimizers”, Information Sciences, Vol. 181, No. 8, pp. 1411–1425, April 15, 2011.

643. K. Sivakumar, C. Balamurugan and S. Ramabalan, “Concurrent multi-objective tolerance allocation of mechanicalassemblies considering alternative manufacturing process selection”, International Journal of Advanced ManufacturingTechnology, Vol. 53, Nos. 5–8, pp. 711–732, March 2011.

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644. Magdalene Marinaki, Yannis Marinakis and Georgios E. Stavroulakis, “Fuzzy control optimized by a Multi-ObjectiveParticle Swarm Optimization algorithm for vibration suppression of smart structures”, Structural and MultidisciplinaryOptimization, Vol. 43, No. 1, pp. 29–42, January 2011.

645. Juan C. Vidal, Manuel Mucientes, Alberto Bugarın and Manuel Lama, “Machine scheduling in custom furniture industrythrough neuro-evolutionary hybridization”, Applied Soft Computing, Vol. 11, No. 2, pp. 1600–1613, March 2011.

646. Mohammad Hamdan, “A dynamic polynomial mutation for evolutionary multi-objective optimization algorithms”, In-ternational Journal on Artificial Intelligence Tools, Vol. 20, No. 1, pp. 209–219, February 2011.

647. Kousik Deb and Anirban Dhar, “Optimum design of stone column-improved soft soil using multiobjective optimizationtechnique”, Computers and Geotechnics, Vol. 38, No. 1, pp. 50–57, January 2011.

648. Carlos R. Garcia-Alonso, Luis Salvador-Carulla, Miguel A. Negrin-Hernandez and Berta Moreno-Kustner, “Developmentof a new spatial analysis tool in mental health: Identification of highly autocorrelated areas (hot-spots) of schizophreniausing a Multiobjective Evolutionary Algorithm model (MOEA/HS)”, Epidemiologia E Psichiatria Sociale–An Interna-tional Journal for Epidemiology and Psychiatric Sciences, Vol. 19, No. 4, pp. 302–313, October-December 2010.

649. Tsung-Che Chiang, Hsueh-Chien Cheng and Li-Chen Fu, “NNMA: An effective memetic algorithm for solving multiob-jective permutation flow shop scheduling problems”, Expert Systems with Applications, Vol. 38, No. 5, pp. 5986–5999,May 2011.

650. J.B. Kollat, P.M. Reed and R.M. Maxwell, “Many-objective groundwater monitoring network design using bias-awareensemble Kalman filtering, evolutionary optimization, and visual analytics”, Water Resources Research, Vol. 47, ArticleNumber: W02529, February 18, 2011.

651. Lei Gao and Atakelty Hailu, “Comprehensive Learning Particle Swarm Optimizer for Constrained Mixed-Variable Opti-mization Problems”, International Journal of Computational Intelligence Systems, Vol. 3, No. 6, pp. 832–842, December2010.

652. Minqiang Li, Liu Liu and Dan Lin, “A fast steady-state epsilon-dominance multi-objective evolutionary algorithm”,Computational Optimization and Applications, Vol. 48, No. 1, pp. 109–138, January 2011.

653. Nguyen Binh Ta Duong, Suiping Zhou, Wentong Cai, Xueyan Tang and Rassul Ayani, “Multi-objective zone mappingin large-scale distributed virtual environments”, Journal of Network and Computer Applications, Vol. 34, No. 2, pp.551–561, March 2011.

654. F. Noori, M. Gorji, A. Kazemi and H. Nemati, “Thermodynamic optimization of ideal turbojet with afterburner enginesusing non-dominated sorting genetic algorithm II”, Proceedings of the Institution of Mechanical Engineers Part G–Journalof Aerospace Engineering, Vol. 224, No. G12, pp. 1285–1296, December 2010.

655. S.-Z. Zhao and P.N. Suganthan, “Two-lbests based multi-objective particle swarm optimizer”, Engineering Optimization,Vol. 43, No. 1, pp. 1–17, January 2011.

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657. Clay Holdsworth, Minsun Kim, Jay Liao Mark H. Phillips, “A hierarchical evolutionary algorithm for multiobjectiveoptimization in IMRT”, Medical Physics, Vol. 37, No. 9, pp. 4986–4997, September 2010.

658. S.H. Yang, U. Natarajan, M. Sekar and S. Palani, “Prediction of surface roughness in turning operations by computer vi-sion using neural network trained by differential evolution algorithm”, International Journal of Advanced ManufacturingTechnology, Vol. 51, Nos. 9–12, pp. 965–971, December 2010.

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660. Kyoung Seok Shin, Jong-Oh Park and Yeo Keun Kim, “Multi-objective FMS process planning with various flexibilitiesusing a symbiotic evolutionary algorithm”, Computers & Operations Research, Vol. 38, No. 3, pp. 702–712, March 2011.

661. Kalyanmoy Deb, Kaisa Miettinen and Shamik Chaudhuri, “Toward an Estimation of Nadir Objective Vector Using aHybrid of Evolutionary and Local Search Approaches”, IEEE Transactions on Evolutionary Computation, Vol. 14, No.6, pp. 821–841, December 2010.

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663. Aris Kornelakis, “Multiobjective Particle Swarm Optimization for the optimal design of photovoltaic grid-connectedsystems”, Solar Energy, Vol. 84, No. 12, pp. 2022–2033, December 2010.

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767. W.M. Chen, H.K. Hwang and T.H. Tsai, “Efficient maxima-finding algorithms for random planar samples”, DiscreteMathematics and Theoretical Computer Science, Vol. 6, No. 1, pp. 107–122, 2003.

768. R. Gras, D. Hernandez, P. Hernandez, N. Zangger, Y. Mescam, J. Frey, O. Martin, J. Nicolas and R.D. Appel, “Co-operative metaheuristics for exploring proteomic data”, Artificial Intelligence Review, Holanda, Vol. 20, Nos. 1–2, pp.95–120, October 2003.

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771. Hisao Ishibuchi and Youhei Shibata, “A Similarity-Based Mating Scheme for Evolutionary Multiobjective Optimization”,in Erick Cantu-Paz et al. (editors), Genetic and Evolutionary Computation—GECCO 2003. Proceedings, Part I, pp.1065–1076, Springer. Lecture Notes in Computer Science Vol. 2723, July 2003.

772. Robin C. Purshouse and Peter J. Fleming, “Conflict, Harmony, and Independence: Relationships in Evolutionary Multi-criterion Optimisation”, in Carlos M. Fonseca, Peter J. Fleming, Eckart Zitzler, Kalyanmoy Deb and Lothar Thiele(editors), Evolutionary Multi-Criterion Optimization. Second International Conference, EMO 2003, pp. 16–30, Springer.Lecture Notes in Computer Science. Volume 2632, Faro, Portugal, April 2003.

773. F. de Negro, J. Ortega, E. Ros, S. Mota, B. Paechter and J.M. Martın, “PSFGA: Parallel processing and evolutionarycomputation for multiobjective optimisation”, Parallel Computing, Holanda, Vol. 30, Nos. 5–6, pp. 721–739, May-June2004.

774. Hisao Ishibuchi and Youhei Shibata, “An Empirical Study on the Effect of Mating Restriction on the Search Abilityof EMO Algorithms”, in Carlos M. Fonseca, Peter J. Fleming, Eckart Zitzler, Kalyanmoy Deb and Lothar Thiele (edi-tors), Evolutionary Multi-Criterion Optimization. Second International Conference, EMO 2003, pp. 433–447, Springer.Lecture Notes in Computer Science. Volume 2632, Faro, Portugal, April 2003.

775. Michael Guntsch and Martin Middendort, “Solving Multi-criteria Optimization Problems with Population-Based ACO”,in Carlos M. Fonseca, Peter J. Fleming, Eckart Zitzler, Kalyanmoy Deb and Lothar Thiele (editors), EvolutionaryMulti-Criterion Optimization. Second International Conference, EMO 2003, pp. 464–478, Springer. Lecture Notes inComputer Science. Volume 2632, Faro, Portugal, April 2003.

776. Kalyanmoy Deb, Pawan Zope and Abhishek Jain, “Distributed Computing of Pareto-Optimal Solutions with Evolution-ary Algorithms”, in Carlos M. Fonseca, Peter J. Fleming, Eckart Zitzler, Kalyanmoy Deb and Lothar Thiele (editors),Evolutionary Multi-Criterion Optimization. Second International Conference, EMO 2003, pp. 534–549, Springer. Lec-ture Notes in Computer Science. Volume 2632, Faro, Portugal, April 2003.

777. Carlos A. Brizuela and Rodrigo Aceves, “Experimental Genetic Operators Analysis for the Multi-objective PermutationFlowshop”, in Carlos M. Fonseca, Peter J. Fleming, Eckart Zitzler, Kalyanmoy Deb and Lothar Thiele (editors), Evo-lutionary Multi-Criterion Optimization. Second International Conference, EMO 2003, pp. 578–592, Springer. LectureNotes in Computer Science. Volume 2632, Faro, Portugal, April 2003.

778. A. Gaspar-Cunha and J.A. Covas, “A Real-World Test Problem for EMO Algorithms”, in Carlos M. Fonseca, PeterJ. Fleming, Eckart Zitzler, Kalyanmoy Deb and Lothar Thiele (editors), Evolutionary Multi-Criterion Optimization.Second International Conference, EMO 2003, pp. 752–766, Springer. Lecture Notes in Computer Science. Volume 2632,Faro, Portugal, April 2003.

779. R.M. Hubley, E. Zitzler and J.C. Roach, “Evolutionary algorithms for the selection of single nucleotide polymorphisms”,BMC Bioinformatics, Inglaterra, Vol. 4, Art. No. 30, July 23, 2003.

780. Andrea Toffolo and Ernesto Benini, “Genetic Diversity as an Objective in Multi-Objective Evolutionary Algorithms”,Evolutionary Computation, Estados Unidos, Vol. 11, No. 2, pp. 151–167, Summer 2003.

781. Hisao Ishibuchi, Tadashi Yoshida and Tadahiko Murata, “Balance Between Genetic Search and Local Search in MemeticAlgorithms for Multiobjective Permutation Flowshop Scheduling”, IEEE Transactions on Evolutionary Computation,Estados Unidos, Vol. 7, No. 2, pp. 204–223, April 2003.

782. T. Wright, V.J. Gillet, D.V.S. Green and S.D. Pickett, “Optimizing the size and configuration of combinatorial libraries”,Journal of Chemical Information and Computer Sciences, Estados Unidos, Vol. 43, No. 2, pp. 381–390, March-April2003.

783. K.C. Tan, E.F. Khor, T.H. Lee and Y.J. Yang, “A tabu-based exploratory evolutionary algorithm for multiobjectiveoptimization”, Artificial Intelligence Review, Holanda, Vol. 19, No. 3, pp. 231–260, May 2003.

784. D. Kim, “Evolving internal memory for T-maze tasks in noisy environments”, Connection Science, Inglaterra, Vol. 16,No. 3, pp. 183–210, September 2004.

785. M. Farina and P. Amato, “Linked interpolation-optimization strategies for multicriteria optimization problems”, SoftComputing–A Fusion of Foundations, Methodologies and Applications, Springer-Verlag, Vol. 9, No. 1, pp. 54–65,January 2005.

786. Hussein A. Abbass, “An Inexpensive Cognitive Approach for Biobjective Optimization using Bliss Points and Interac-tion”, in Xin Yao et al. (editors), Parallel Problem Solving from Nature - PPSN VIII, Springer-Verlag, Lecture Notes inComputer Science, Vol. 3242, pp. 712–721, September 2004.

787. Frank Schlottmann and Detlef Seese, “A hybrid heuristic approach to discrete multi-objective optimization of creditportfolios”, Computational Statistics & Data Analysis, Holanda, Vol. 47, No. 2, pp. 373–399, September 1, 2004.

788. Tatsuya Okabe, Yaochu Jin, Markus Olhofer and Bernhard Sendhoff, “On Test Functions for Evolutionary Multi-Objective Optimization”, in Xin Yao et al. (editors), Parallel Problem Solving from Nature - PPSN VIII, Springer-Verlag,Lecture Notes in Computer Science, Vol. 3242, pp. 792–802, September 2004.

789. J. Ku, X.J. Feng and H. Rabitz, “Closed-loop learning control of bio-networks”, Journal of Computational Biology,Estados Unidos, Vol. 11, No. 4, pp. 642–659, 2004.

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790. Carlos Garcıa-Martinez, Oscar Cordon and Francisco Herrera, “An Empirical Analysis of Multiple Objective Ant ColonyOptimization Algorithms for the Bi-criteria TSP”, in Marco Dorigo, Mauro Birattari, Christian Blum, Luca M. Gam-bardella, Francesco Mondada and Thomas Stutzle (editors), Proceedings of the 4th International Workshop on AntColony Optimization and Swarm Intelligence, ANTS 2004, Belgica, Springer, Lecture Notes in Computer Science, Vol.3172, pp. 61–72, 2004.

791. D. Greiner, J.M. Emperador and G. Winter, “Single and multiobjective frame optimization by evolutionary algorithmsand the auto-adaptive rebirth operator”, Computer Methods in Applied Mechanics and Engineering, Suiza, Vol. 193,Nos. 33–35, pp. 3711–3743, 2004.

792. R.B. Kasat and S.K. Gupta, “Multi-objective optimization of an industrial fluidized-bed catalytic cracking unit (FCCU)using genetic algorithm (GA) with the jumping genes operator”, Computers & Chemical Engineering, Inglaterra, Vol.27, No. 12, pp. 1785–1800, December 15, 2003.

793. Eric M. Koper, William D. Wood and Stephen W. Schneider, “Aircraft antenna coupling minimization using geneticalgorithms and approximations”, IEEE Transactions on Aerospace and Electronic Systems, Estados Unidos, Vol. 40,No. 2, pp. 742–751, April 2004.

794. J. Mehnen, T. Micheltisch, T. Bartz-Beielstein and K. Schmitt, “Evolutionary optimization of mould temperature controlstrategies: encoding and solving the multiobjective problem with standard evolution strategy and kit for evolution algo-rithms”, Proceedings of the Institution of Mechanical Engineers Part B—Journal of Engineering Manufacture, Inglaterra,Vol. 218, No. 6, pp. 657–665, June 2004.

795. Karim Hamza and Kazuhiro Saitou, “Optimization of Constructive Solid Geometry Via a Tree-Based Multi-objectiveGenetic Algorithm”, in Kalyanmoy Deb et al. (editors), Genetic and Evolutionary Computation–GECCO 2004. Pro-ceedings of the Genetic and Evolutionary Computation Conference. Part II, Springer-Verlag, Lecture Notes in ComputerScience Vol. 3103, pp. 981–992, Seattle, Washington, USA, June 2004.

796. Julien Frey, Robin Gras, Patricia Hernandez and Ron Appel, “A hierarchical model of parallel genetic programmingapplied to bioinformatic problems”, in Roman Wyrzykowski, Jack Dongarra, Marcin Paprzycki et al. (editors) ParallelProcessing and Applied Mathematics: 5th International Conference (PPAM 2003), Polonia, Springer, Lecture Notes inComputer Science Vol. 3019, pp. 1146–1153, 2003.

797. P. Morillo, J.M. Orduna and M. Fernandez, “A comparison study of evolutive algorithms for solving the partitioningproblem in distributed virtual environment systems”, Parallel Computing, Holanda, Vol. 30, Nos. 5–6, pp. 585–610,May-June 2004.

798. A. Suppapitnarm, G.T. Parks, K. Shea and P.J. Clarkson, “Conceptual Design of Bicycle Frames by MultiobjectiveShape Annealing”, Engineering Optimization, Vol. 36, No. 2, pp. 165–188, April 2004.

799. H. Ishibuchi and T. Yamamoto, “Interpretability issues in fuzzy genetics-based machine learning for linguistic modelling”,in Modelling with Words: Learning, Fusion, and Reasoning within a Formal Linguistic Representation Framework,Springer-Verlag, Lecture Notes in Artificial Intelligence, Vol. 2873, pp. 209–228, 2003.

800. Hussein A. Abbass, “Pareto neuro-ensembles”, AI 2003: Advances in Artificial Intelligence, Australia, Lecture Notes inArtificial Intelligence, Vol 2903, pp. 554–566, 2003.

801. O. Cordon, F. Gomide, F. Herrera, F. Hoffmann and L. Magdalena, “Ten years of genetic fuzzy systems: currentframework and new trends”, Fuzzy Sets and Systems, Holanda, Vol. 141, No. 1, pp. 5–31, January 1, 2004.

802. O. Cordon, E. Herrera-Viedma, M. Luque, F. de Moya and C. Zarco, “Analyzing the performance of a multiobjectiveGA-P algorithm for learning fuzzy queries in a machine learning environment”, in Proceedings of Fuzzy Sets and Systems(IFSA 2003), Turquıa, Springer, Lecture Notes in Artificial Intelligence, Vol. 2715, pp. 611–619, 2003.

803. H.A. Abbass, “Speeding up backpropagation using multiobjective evolutionary algorithms”, Neural Computation, Vol.15, No. 11, pp. 2705–2726, November 2003.

804. Xavier Llora and David E. Goldberg, “Bounding the Effect of Noise in Multiobjective Learning Classifier Systems”,Evolutionary Computation, Estados Unidos, Vol. 11, No. 3, pp. 279–298, Fall 2003.

805. Karim Hamza, Juan F. Reyes-Luna and Kazuhiro Saitou, “Simultaneous Assembly Planning and Assembly SystemDesign Using Multi-objective Genetic Algorithms”, in Erick Cantu-Paz et al. (editors), Genetic and EvolutionaryComputation—GECCO 2003. Proceedings, Part II, pp. 2096–2108, Springer. Lecture Notes in Computer Science Vol.2724, July 2003.

806. Martin Brown and Robert E. Smith, “Effective Use of Directional Information in Multi-objective Evolutionary Compu-tation”, in Erick Cantu-Paz et al. (editors), Genetic and Evolutionary Computation—GECCO 2003. Proceedings, PartI, pp. 778–789, Springer. Lecture Notes in Computer Science Vol. 2723, July 2003.

807. Andrew Wildman and Geoff Parks, “A Comparative Study of Selective Breeding Strategies in a Multiobjective GeneticAlgorithm”, in Carlos M. Fonseca, Peter J. Fleming, Eckart Zitzler, Kalyanmoy Deb and Lothar Thiele (editors), Evo-lutionary Multi-Criterion Optimization. Second International Conference, EMO 2003, pp. 418–432, Springer. LectureNotes in Computer Science. Volume 2632, Faro, Portugal, April 2003.

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808. Andrzej Jaszkiewicz, “Do Multiple-Objective Metaheuristics Deliver on Their Promises? A Computational Experimenton the Set-Covering Problem”, IEEE Transactions on Evolutionary Computation, Vol. 7, No. 2, pp. 133–143, April2003.

809. S. O’Hagan, W.B. Dunn, M. Brown, J.D. Knowles and D.B. Kell, “Closed-loop, multiobjective optimization of analyticalinstrumentation: Gas chromatography / time-of-flight mass spectrometry of the metabolomes of human serum and ofyeast fermentations”, Analytical Chemistry, Vol. 77, No. 1, pp. 290–303, January 1, 2005.

810. Taghi M. Khoshgoftaar, Yi Liu and Naeem Seliya, “A Multiobjective Module-Order Model for Software Quality En-hancement”, IEEE Transactions on Evolutionary Computation, Estados Unidos, Vol. 8, No. 6, pp. 593–608, December2004.

811. H. Aguirre and K. Tanaka, “Random bit climbers on multiobjective MNK-Landscapes: Effects of memory and populationclimbing”, IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, Vol. E88A,No. 1, pp. 334–345, January 2005.

812. S. Gunawan and S. Azarm, “Multi-objective robust optimization using a sensitivity region concept”, Structural andMultidisciplinary Optimization, Vol. 29, No. 1, pp. 50–60, January 2005.

813. R.F. Coelho and P. Bouillard, “A multicriteria evolutionary algorithm for mechanical design optimization with expertrules”, International Journal for Numerical Methods in Engineering, Vol. 62, No. 4, pp. 516–536, January 28, 2005.

814. D.G. Mayer, B.P. Kinghorn and A.A. Archer, “Differential evolution - an easy and efficient evolutionary algorithm formodel optimisation”, Agricultural Systems, Vol. 83, No. 3, pp. 315–328, March 2005.

815. L. Luo, P.K. Kannan, B. Besharati and S. Azarm, “Design of robust new products under variability: Marketing meetsdesign”, Journal of Product Innovation Management, Vol. 22, No. 2, pp. 177–192, March 2005.

816. R. Kumar, R.K. Singh and P.P. Chakrabarti, “Improved quality of solutions for multiobjective spanning tree problemusing distributed evolutionary algorithm”, High Performance Computing - HIPC 2004, Springer-Verlag, Lecture Notesin Computer Science Vol. 3296, pp. 494–503, 2004.

817. L. Samaniego and A. Bardossy, “Robust parametric models of runoff characteristics at the mesoscale”, Journal ofHydrology, Vol. 303, Nos. 1-4, pp. 136–151, March 1, 2005.

818. Hui Li and Qingfu Zhang, “Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II”, IEEE Transactions on Evolutionary Computation, Vol. 13, No. 2, pp. 284–302, April 2009.

819. Ricardo Perera, Antonio Ruiz and Carlos Manzano, “Performance assessment of multicriteria damage identificationgenetic algorithms”, Computers & Structures, Vol. 87, Nos. 1-2, pp. 120–127, January 2009.

820. Asish Kumar Sharma, Chandramouli Kulshreshtha, Keemin Sohn and Kee-Sun Sohn, “Systematic Control of Exper-imental Inconsistency in Combinatorial Materials Science”, Journal of Combinatorial Chemistry, Vol. 11, No. 1, pp.131–137, January-February 2009.

821. Wasim Raza and Kwang-Yong Kim, “Shape Optimization of 19-Pin Wire-Wrapped Fuel Assembly of LMR Using Mul-tiobjective Evolutionary Algorithm”, Nuclear Science and Engineering, Vol. 161, No. 2, pp. 245–254, February 2009.

822. Kishalay Mitra, Sushanta Majumder and Venkatarama Runkana, “Multiobjective Pareto Optimization of an IndustrialStraight Grate Iron Ore Induration Process Using an Evolutionary Algorithm”, Materials and Manufacturing Processes,Vol. 24, No. 3, pp. 331–342, 2009.

823. Andres L. Medaglia, Juan G. Villegas and Diana M. Rodriguez-Coca, “Hybrid biobjective evolutionary algorithms forthe design of a hospital waste management network”, Journal of Heuristics, Vol. 15, No. 2, pp. 153–176, April 2009.

824. M. Katebi, H. Tawfik and S.D. Katebi, “Limit Cycle Prediction Based on Evolutionary Multiobjective Formulation”,Mathematical Problems in Engineering, Article Number 816707, 2009.

825. Severino F. Galan and Ole J. Mengshoel, “Constraint Handling Using Tournament Selection: Abductive Inference inPartly Deterministic Bayesian Networks”, Evolutionary Computation, Vol. 17, No. 1, pp. 55–88, Spring 2009.

826. R. Banos, C. Gil, J. Reca and J. Martınez, “Implementation of scatter search for multi-objective optimization: acomparative study”, Computational Optimization and Applications, Vol. 42, No. 3, pp. 421–441, April 2009.

827. Eduardo Raul Hruschka, Ricardo J.G.B. Campello, Alex A. Freitas, Andre C. Ponce de Leon F. de Carvalho, “A Surveyof Evolutionary Algorithms for Clustering”, IEEE Transactions on Systems, Man, and Cybernetics Part C—Applicationsand Reviews, Vol. 39, No. 2, pp. 133–155, March 2009.

828. Hisao Ishibuchi, Yasuhiro Hitotsuyanagi, Noritaka Tsukamoto and Yusuke Nojima, “Use of biased neighborhood struc-tures in multiobjective memetic algorithms”, Soft Computing, Vol. 13, Nos. 8–9, pp. 795–810, July 2009.

829. S.C. Chiam, K.C. Tan, C.K. Goh and A. Al Mamun, “Improving locality in binary representation via redundancy”,IEEE Transactions on Systems, Man, and Cybernetics Part B–Cybernetics, Vol. 38, No. 3, pp. 808–825, June 2008.

830. Rafael Munoz-Salinas, Eugenio Aguirre, Oscar Cordon and Miguel Garcia-Silvente, “Automatic tuning of a fuzzy visualsystem using evolutionary-algorithms: Single-objective versus multiobjective approaches”, IEEE Transactions on FuzzySystems, Vol. 16, No. 2, pp. 485–501, April 2008.

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831. Joana Dias, M. Eugenia Captivo and Joao Climaco, “A memetic algorithm for multi-objective dynamic location prob-lems”, Journal of Global Optimization, Vol. 42, No. 2, pp. 221–253, October 2008.

832. Feili Yu, Fang Tu, Krishna R. Pattipati, “Integration of a holonic organizational control architecture and multiobjectiveevolutionary algorithm for flexible distributed scheduling”, IEEE Transactions on Systems, Man, and Cybernetics PartA–Systems and Humans, Vol. 38, No. 5, pp. 1001–1017, September 2008.

833. Christian Gagne, Julie Beaulieu, Marc Parizeau and Simon Thibault, “Human-competitive lens system design withevolution strategies”, Applied Soft Computing, Vol. 8, No. 4, pp. 1439–1452, September 2008.

834. Leandro dos Santos Coelho and Piergiorgio Alotto, “Multiobjective electromagnetic optimization based on a nondom-inated sorting genetic approach with a chaotic crossover operator”, IEEE Transactions on Magnetics, Vol. 44, No. 6,pp. 1078–1081, June 2008.

835. Laetitia Jourdan, Oliver Schuetze, Thomas Legrand, El-Ghazali Talbi and Jean Luc Wojkiewicz, “An Analysis of the Ef-fect of Multiple Layers in the Multi-Objective Design of Conducting Polymer Composites”, Materials and ManufacturingProcesses, Vol. 24, No. 3, pp. 350–357, 2009.

836. Oliver Schuetze, Laetitia Jourdan, Thomas Legrand, El-Ghazali Talbi and Jean-Luc Wojkiewicz, “New analysis ofthe optimization of electromagnetic shielding properties using conducting polymers and a multi-objective approach”,Polymers for Advanced Technologies, Vol. 19, No. 7, pp. 762–769, July 2008.

837. Frank Pettersson, Arijit Biswas, Prodip Kumar Sen, Henrik Saxen and Nirupam Chakraborti, “Analyzing Leaching Datafor Low-Grade Manganese Ore Using Neural Nets and Multiobjective Genetic Algorithms”, Materials and ManufacturingProcesses, Vol. 24, No. 3, pp. 320–330, March 2009.

838. Akash Agarwal, Frank Pettersson, Arunima Singh, Chang Sun Kong, Henrik Saxen, Krishna Rajan, Shuichi Iwata andNirupam Chakraborti, “Identification and Optimization of AB2 Phases Using Principal Component Analysis, Evolution-ary Neural Nets, and Multiobjective Genetic Algorithms”, Materials and Manufacturing Processes, Vol. 24, No. 3, pp.274–281, March 2009.

839. Oliver Schutze, Massimiliano Vasile, Oliver Junge, Michael Dellnitz and Dario Izzo, “Designing optimal low-thrustgravity-assist trajectories using space pruning and a multi-objective approach”, Engineering Optimization, Vol. 41, No.2, pp. 155–181, February 2009.

840. Jesus Garcıa Herrero, Antonio Berlanga and Jose Manuel Molina Lopez, “Effective Evolutionary Algorithms for Many-Specifications Attainment: Application to Air Traffic Control Tracking Filters”, IEEE Transactions on EvolutionaryComputation, Vol. 13, No. 1, pp. 151–168, February 2009.

841. Lam T. Bui, Hussein A. Abbass and Daryl Essam, “Local models—an approach to distributed multi-objective optimiza-tion”, Computational Optimization and Applications, Vol. 42, No. 1, pp. 105–139, January 2009.

842. Rocıo C. Romero-Zaliz, Cristina Rubio-Escudero, J. Perren Cobb, Francisco Herrera, Oscar Cordon and Igor Zwir, “AMultiobjective Evolutionary Conceptual Clustering Methodology for Gene Annotation Within Structural Databases: ACase Study on the Gene Ontology Database”, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 6, pp.679–701, December 2008.

843. R. Brits, A.P. Engelbrecht and F. van den Bergh, “Locating multiple optima using particle swarm optimization”, AppliedMathematics and Computation, Vol. 189, No. 2, pp. 1859–1883, June 15, 2007.

844. K. Mitra, “Genetic algorithms in polymeric material production, design, processing and other applications: a review”,International Materials Review, Vol. 53, No. 5, pp. 275–297, September 2008.

845. J.M. Nobrega, O.S. Carneiro, A. Gaspar-Cunha and N.D. Goncalves, “Design of calibrators for profile extrusion -Optimizing multi-step systems”, International Polymer Processing, Vol. 23, No. 3, pp. 331–338, July 2008.

846. Shao Yong Zheng, Sai Ho Yeung, Wing Shing Chan, Kim Fung Man, Shu Hung Leung and Quan Xue, “Dual-bandrectangular patch hybrid coupler”, IEEE Transactions on Microwave Theory and Techniques, Vol. 56, No. 7, pp.1721–1728, July 2008.

847. S.Y.S. Leung, W.K. Wong and P.Y. Mok, “Multiple-objective genetic optimization of the spatial design for packing anddistribution carton boxes”, Computers & Industrial Engineering, Vol. 54, No. 4, pp. 889–902, May 2008.

848. Xiufen Zou, Yu Chen, Minzhong Liu and Lishan Kang, “A New Evolutionary Algorithm for Solving Many-ObjectiveOptimization Problems”, IEEE Transactions on Systems, Man, and Cybernetics–Part B: Cybernetics, Vol. 38, No. 5,pp. 1402–1412, October 2008.

849. N. Chakraborti, A. Shekhar, A. Singhal, S. Chakraborty, S. Chowdhury and R. Sripriya, “Fluid flow in hydrocyclonesoptimized through multi-objective genetic algorithms”, Inverse Problems in Science and Engineering, Vol. 16, No. 8,pp. 1023–1046, December 2008.

850. Min Zhang, Wenjian Luo and Xufa Wang, “Differential evolution with dynamic stochastic selection for constrainedoptimization”, Information Sciences, Vol. 178, No. 15, pp. 3043–3074, August 1, 2008.

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851. Elizabeth F. Wanner, Frederico G. Guimaraes, Ricardo H.C. Takahashi and Peter J. Fleming, “Local Search withQuadratic Approximations into Memetic Algorithms for Optimization with Multiple Criteria”, Evolutionary Computa-tion, Vol. 16, No. 2, pp. 185–224, Summer 2008.

852. Antonio J. Nebro, Francisco Luna, Enrique Alba, Bernabe Dorronsoro, Juan J. Durillo and Andreas Beham, “AbYSS:Adapting Scatter Search to Multiobjective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 12,No. 4, pp. 439–457, August 2008.

853. Frederic Ros, Serge Guillaume, Marco Pintore and Jacques R. Chretien, “Hybrid genetic algorithm for dual selection”,Pattern Analysis and Applications, Vol. 11, No. 2, pp. 179–198, June 2008.

854. Maoguo Gong, Licheng Jiao, Haifeng Du and Liefeng Bo, “Multiobjective immune algorithm with nondominatedneighbor-based selection”, Evolutionary Computation, Vol. 16, No. 2, pp. 225–255, Summer 2008.

855. Mohammed Khabzaoui, Clarisse Dhaenens and El-Ghazali Talbi, “Combining evolutionary algorithms and exact ap-proaches for multi-objective knowledge discovery”, RAIRO–Operations Research, Vol. 42, No. 1, pp. 69–83, January-March 2008.

856. J. Reca, J. Martinez, R. Banos and C. Gil, “Optimal design of gravity-fed looped water distribution networks consideringthe resilience index”, Journal of Water Resources Planning and Management–ASCE, Vol. 134, No. 3, pp. 234–238,May-June 2008.

857. Sanghamitra Bandyopadhyay, Sriparna Saha, Ujjwal Maulik and Kalyanmoy Deb, “A Simulated Annealing-Based Mul-tiobjective Optimization Algorithm: AMOSA”, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 3, pp.269–283, June 2008.

858. N. Chakraborti, B. Siva Kumar, V. Satish Babu, S. Moitra and A. Mukhopadhyay, “A new multi-objective geneticalgorithm applied to hot-rolling process”, Applied Mathematical Modelling, Vol. 32, No. 9, pp. 1781–1789, September2008.

859. Hamidreza Eskandari and Christopher D. Geiger, “A fast Pareto genetic algorithm approach for solving expensivemultiobjective optimization problems”, Journal of Heuristics, Vol. 14, No. 3, pp. 203–241, June 2008.

860. Philipp Limbourg and Hans-Dieter Kochs, “Multi-objective optimization of generalized reliability design problems usingfeature models - A concept for early design stages”, Reliability Engineering & System Safety, Vol. 93, No. 6, pp. 815–828,June 2008.

861. Jun Guo, Yi Wang, Kit-Sang Tang, Sammy Chan, Eric W.M. Wong, Peter Taylor and Moshe Zukerman, “Evolutionaryoptimization of file assignment for a large-scale video-on-demand system”, IEEE Transactions on Knowledge and DataEngineering, Vol. 20, No. 6, pp. 836–850, June 2008.

862. Sai-Ho Yeung, Hoi-Kuen Ng and Kim-Fung Man, “Multi-criteria design methodology of a dielectric resonator antennawith jumping genes evolutionary algorithm”, AEU-International Journal of Electronics and Communications, Vol. 62,No. 4, pp. 266–276, 2008.

863. David S. Robin, Wan Weishi Wan, Fernando Sannibale and Victor P. Suller, “Global analysis of all linear stable settingsof a storage ring lattice”, Physical Review Special Topics–Accelerators and Beams, Vol. 11, No. 2, Article Number024002, February 2008.

864. Nicolas Jozefowiez, Frederic Semet and El-Ghazali Talbi, “Multi-objective vehicle routing problems”, European Journalof Operational Research, Vol. 189, No. 2, pp. 293–309, September 1, 2008.

865. Miguel Delgado, Manuel P. Cuellar and Maria Carmen Pegalajar, “Multiobjective hybrid optimization and training ofrecurrent neural Networks”, IEEE Transactions on Systems, Man, and Cybernetics–Part B: Cybernetics, Vol. 38, No.2, pp. 381–403, April 2008.

866. Xingdong Zhang and Marc P. Armstrong, “Genetic algorithms and the corridor location problem: multiple objectivesand alternative solutions”, Environment and Planning B–Planning & Design, Vol. 35, No. 1, pp. 148–168, January2008.

867. Wasim Raza and Kwang-Yong Kim, “Multiobjective optimization of a wire-wrapped LMR fuel assembly”, NuclearTechnology, Vol. 162, No. 1, pp. 45–52, April 2008.

868. J.M. Herrero, X. Blasco, M. Martinez, C. Ramos and J. Sanchis, “Robust identification of non-linear greenhouse modelusing evolutionary algorithms”, Control Engineering Practice, Vol. 16, No. 5, pp. 515–530, May 2008.

869. Ben Torben-Nielsen, Karl Tuyls and Eric Postma, “EvOL-NEURON: Neuronal morphology generation”, Neurocomput-ing, Vol. 71, Nos. 4–6, pp. 963–972, January 2008.

870. Fabian Duddeck, “Multidisciplinary optimization of car bodies”, Structural and Multidisciplinary Optimization, Vol. 35,No. 4, pp. 375–389, April 2008.

871. Ricardo Perera and Antonio Ruiz, “A multistage FE updating procedure for damage identification in large-scale struc-tures based on multiobjective evolutionary optimization”, Mechanical Systems and Signal Processing, Vol. 22, No. 4,pp. 970–991, May 2008.

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872. Gio J. Kao and Sheldon H. Jacobson, “Finding preferred subsets of Pareto optimal solutions”, Computational Optimiza-tion and Applications, Vol. 40, No. 1, pp. 73–95, May 2008.

873. A. I. Olcer, “A hybrid approach for multi-objective combinatorial optimisation problems in ship design and shipping”,Computers & Operations Research, Vol. 35, No. 9, pp. 2760–2775, September 2008.

874. Hisao Ishibuchi, Kaname Narukawa, Noritaka Tsukamoto and Yusuke Nojima, “An empirical study on similarity-basedmating for evolutionary multiobjective combinatorial optimization”, European Journal of Operational Research, Vol.188, No. 1, pp. 57–75, July 1, 2008.

875. Annette Muetze, “A neglected stepchild”, IEEE Industry Applications Magazine, Vol. 14, No. 2, pp. 14–22, March-April2008.

876. N. Amanifard, N. Nariman-Zadeh, M. Borji, A. Khalkhali and A. Habibdoust, “Modelling and Pareto optimization ofheat transfer and flow coefficients in microchannels using GMDH type neural networks and genetic algorithms”, EnergyConversion and Management, Vol. 49, No. 2, pp. 311–325, February 2008.

877. N. Nariman-zadeh, A. Jamali and A. Hajiloo, “Frequency-based reliability Pareto optimum design of proportional-integral-derivative controllers for systems with probabilistic uncertainty”, Proceedings of the Institution of MechanicalEngineers Part I–Journal of Systems and Control Engineering, Vol. 221, No. I8, pp. 1061–1075, December 2007.

878. Shubhabrata Datta, Frank Pettersson, Subhas Ganguly, Henrik Saxen and Nirupam Chakraborti, “Identification offactors governing mechanical properties of TRIP-aided steel using genetic algorithms and neural networks”, Materialsand Manufacturing Processes, Vol. 23, No. 2, pp. 131–138, 2008.

879. Bin Qian, Ling Wang, De-Xian Huang and Xiong Wang, “Scheduling multi-objective job shops using a memetic algorithmbased on differential evolution”, International Journal of Advanced Manufacturing Technology, Vol. 35, Nos. 9–10, pp.1014–1027, January 2008.

880. O. Giustolisi, A. Doglioni, D.A. Savic and F. di Pierro, “An evolutionary multiobjective strategy for the effectivemanagement of groundwater resources”, Water Resources Research, Vol. 44 No. 1, article number W01403, January 3,2008.

881. Eduardo Fernandez, Nora Cancela and Rafael Olmedo, “Deriving a final ranking from fuzzy preferences: An approachcompatible with the Principle of Correspondence”, Mathematical and Computer Modelling, Vol. 47, Nos. 1–2, pp.218–234, January 2008.

882. Javier Sanchis, Miguel A. Martinez and Xavier Blasco, “Integrated multiobjective optimization and a priori preferencesusing genetic algorithms”, Information Sciences, Vol. 178, No. 4, pp. 931–951, February 15, 2008.

883. J. Sanchis, M. Martinez and X. Blasco, “Multi-objective engineering design using preferences”, Engineering Optimization,Vol. 40, No. 3, pp. 253–269, 2008.

884. Paulo Fazendeiro, Jose Valente de Oliveira and Witold Pedrycz, “A multiobjective design of a patient and anaesthetist-friendly neuromuscular blockade controller”, IEEE Transactions on Biomedical Engineering, Vol. 54, No. 9, pp. 1667–1678, September 2007.

885. Zbigniew Michalewicz and Matthew Michalewicz, “Machine intelligence, adaptive business intelligence, and naturalintelligence”, IEEE Computational Intelligence Magazine, Vol. 3, No. 1, pp. 54–63, 2008.

886. F. Pettersson, N. Chakraborti and S.B. Singh, “Neural Networks Analysis of Steel Plate Processing Augmented byMulti-objective Genetic Algorithms”, Steel Research International, Vol. 78, No. 12, pp. 890–898, December 2007.

887. Qingfu Zhang, Aimin Zhou and Yaochu Jin, “RM-MEDA: A Regularity Model-Based Multiobjective Estimation ofDistribution Algorithm”, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 1, pp. 41–63, February 2008.

888. A. Gaspar-Cunha and J.A. Covas, “Robustness in multi-objective optimization using evolutionary algorithms”, Compu-tational Optimization and Applications, Vol. 39, No. 1, pp. 75–96, January 2008.

889. Genci Capi, “Multiobjective evolution of neural controllers and task complexity”, IEEE Transactions on Robotics, Vol.23, No. 6, pp. 1225–1234, 2007.

890. J.M. Herrero, X. Blasco, M. Martinez, C. Ramos and J. Sanchis, “Non-linear robust identification of a greenhouse modelusing multi-objective evolutionary algorithms”, Biosystems Engineering, Vol. 98, No. 3, pp. 335–346, November 2007.

891. Ang Yang, Hussein A. Abbass and Ruhul Sarker, “Characterizing warfare in red teaming”, IEEE Transactions onSystems, Man, and Cybernetics, Part B–Cybernetics, Vol. 36, No. 2, pp. 268–285, April 2006.

892. Julian Molina, Manuel Laguna, Rafael Marti and Rafael Caballero, “SSPMO: A scatter tabu search procedure fornon-linear multiobjective optimization”, INFORMS Journal on Computing, Vol. 19, No. 1, pp. 91–100, January 2007.

893. Robic C. Purshouse and Peter J. Fleming, “On the Evolutionary Optimization of Many Conflicting Objectives”, IEEETransactions on Evolutionary Algorithms, Vol. 11, No. 6, pp. 770–784, December 2007.

894. Qingfu Zhang and Hui Li, “MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition”, IEEETransactions on Evolutionary Computation, Vol. 11, No. 6, pp. 712–731, December 2007.

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895. Sebastian Ventura, Cristobal Romero, Amelia Zafra, Jose A. Delgado and Cesar Hervas, “JCLEC: a Java framework forevolutionary computation”, Soft Computing, Vol. 12, No. 4, pp. 381–392, February 2008.

896. Joshua B. Kollat and Patrick Reed, “A framework for visually interactive decision-making and design using evolutionarymulti-objective optimization (VI(D)under-barEO)”, Environmental Modelling & Software, Vol. 22, No. 12, pp. 1691–1704, December 2007.

897. Marco A. Panduro, Carlos A. Brizuela and David H. Covarrubias, “Design of electronically steerable linear arrays withevolutionary algorithms”, Applied Soft Computing, Vol. 8, No. 1, pp. 46–54, January 2008.

898. Kay-Soon Low and Tze-Shyan Wong, “A multiobjective genetic algorithm for optimizing the performance of hard diskdrive motion control system”, IEEE Transactions on Industrial Electronics, Vol. 54, No. 3, pp. 1716–1725, June 2007.

899. Arlene G. Smithson, Karim Hamza and Kazuhiro Saitou, “Design for existing lines: Part and process plan optimizationto best utilize existing production lines”, Journal of Computing and Information Science in Engineering, Vol. 7, No. 2,pp. 126–131, June 2007.

900. DaeEun Kim and Jaehong Park, “Application of adaptive control to the fluctuation of engine speed at idle”, InformationSciences, Vol. 177, No. 16, pp. 3341–3355, August 15, 2007.

901. A.F. Gomez-Skarmeta, F. Jimenez and G. Sanchez, “Improving interpretability in approximative fuzzy models viamultiobjective evolutionary algorithms”, International Journal of Intelligent Systems, Vol. 22, No. 9, pp. 943–969,September 2007.

902. Sarbajit Pal, Pankaj Ganguly and P.K. Biswas, “Cubic Bezier approximation of a digitized curve”, Pattern Recognition,Vol. 40, No. 10, pp. 2730–2741, October 2007.

903. Maria Jose del Jesus, Pedro Gonzalez, Francisco Herrera and Mikel Mesonero, “Evolutionary fuzzy rule induction processfor subgroup discovery: A case study in marketing”, IEEE Transactions on Fuzzy Systems, Vol. 15, No. 4, pp. 578–592,August 2007.

904. Luciano Sanchez and Ines Couso, “Advocating the use of imprecisely observed data in genetic fuzzy systems”, IEEETransactions on Fuzzy Systems, Vol. 15, No. 4, pp. 551–562, August 2007.

905. A. Tarafder, G.P. Rangaiah and Ajay K. Ray, “A study of finding many desirable solutions in multiobjective optimizationof chemical processes”, Computers & Chemical Engineering, Vol. 31, No. 10, pp. 1257–1271, October 2007.

906. S. Dehuri, S. Patnaik, A. Ghosh and R. Mall, “Application of elitist multi-objective genetic algorithm for classificationrule generation”, Applied Soft Computing, Vol. 8, No. 1, pp. 477–487, January 2008.

907. Ricardo Perera, Antonio Ruiz and Carlos Manzano, “An evolutionary multiobjective framework for structural damagelocalization and quantification”, Engineering Structures, Vol. 29, No. 10, pp. 2540–2550, October 2007.

908. M. Ye and G. Zhouz, “A local genetic approach to multi-objective, facility layout problems with fixed aisles”, Interna-tional Journal of Production Research, Vol. 45, No. 22, pp. 5243–5264, 2007.

909. Guangtao Fu, David Butler and Soon-Thiam Khu, “Multiple objective optimal control of integrated urban wastewatersystems”, Environmental Modelling & Software, Vol. 23, No. 2, pp. 225–234, February 2008.

910. Yuren Zhou and Jun He, “Convergence analysis of a self-adaptive multi-objective evolutionary algorithm based on grids”,Information Processing Letters, Vol. 104, No. 4, pp. 117–122, November 15, 2007.

911. David Midgley, Robert Marks and Dinesh Kunchamwar, “Building and assurance of agent-based models: An exampleand challenge to the field”, Journal of Business Research, Vol. 60, No. 8, pp. 884–893, August 2007.

912. Shubhabrata Datta, Frank Pettersson, Subhas Ganguly, Henrik Saxen and Niruopam Chakraborti, “Designing HighStrength Multi-phase Steel for Improved Strength-Ductility Balance Using Neural Networks and Multi-objective GeneticAlgorithms”, ISIJ International, Vol. 47, No. 8, pp. 1195–1203, 2007

913. Yandra and Hiroyuki Tamura, “A new multiobjective genetic algorithm with heterogeneous population for solvingflowshop scheduling problems”, International Journal of Computer Integrated Manufacturing, Vol. 20, No. 5, pp.465–477, 2007.

914. Frederico G. Guimaraes, Reinaldo M. Palhares, Felipe Campelo and Hajime Igarashi, “Design of mixed H-2/H infinitycontrol systems using algorithms inspired by the immune system”, Information Sciences, Vol. 177, No. 20, pp. 4368–4386, October 15, 2007.

915. Kumara Sastry, D.D. Johnson, Alexis L. Thompson, David E. Goldberg, Todd J. Martinez, Jeff Leiding and JaneOwens, “Optimization of Semiempirical Quantum Chemistry Methods via Multiobjective Genetic Algorithms: AccuratePhotodynamics for Larger Molecules and Longer Time Scales”, Materials and Manufacturing Processes, Vol. 22, No. 5,pp. 553–561, 2007.

916. Henrik Saxen, Frank Pettersson and Kiran Gunturu, “Evolving Nonlinear Time-Series Models of the Hot Metal SiliconContent in the Blast Furnace”, Materials and Manufacturing Processes, Vol. 22, Nos. 5-6, pp. 577–584, 2007.

917. Kaisa Miettinen, “Using Interactive Multiobjective Optimization in Continuous Casting of Steel”, Materials and Man-ufacturing Processes, Vol. 22, No. 5, pp. 585–593, 2007.

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918. S. Ganguly, S. Datta and N. Chakraborti, “Genetic algorithms in optimization of strength and ductility of low-carbonsteels”, Materials and Manufacturing Processes, Vol. 22, Nos. 5–6, pp. 650–658, 2007.

919. Eleni Aggelogiannaki and Haralarnbos Sarimveis, “Simulated annealing algorithm for prioritized multiobjective optimization-implementation in an adaptive model predictive control configuration”, IEEE Transactions on Systems, Man, and Cy-bernetics Part B–Cybernetics, Vol. 37, No. 4, pp. 902–915, August 2007.

920. Christian Gagne and Marc Parizeau, “Genetic engineering of hierarchical fuzzy regional representations for handwrittencharacter recognition”, International Journal on Document Analysis and Recognition, Vol. 8, No. 4, pp. 223–231,September 2006.

921. Don Jyh-Fu Jeng, Ikno Kim and Junzo Watada, “Bio-soft computing with fixed-length DNA to a group control opti-mization problem”, Soft Computing, Vol. 12, No. 3, pp. 223–228, February 2008.

922. Antonio Pinto, Daniele Peri and Emilio F. Campana, “Multiobjective optimization of a containership using deterministicparticle swarm optimization”, Journal of Ship Research, Vol. 51, No. 3, pp. 217–228, September 2007.

923. Jing Liu, Weicai Zhong and Licheng Hao, “An organizational evolutionary algorithm for numerical optimization”, IEEETransactions on Systems, Man and Cybernetics Part B–Cybernetics, Vol. 37, No. 4, pp. 1052–1064, August 2007.

924. L.F. Gonzalez, J. Periaux, L. Damp and K. Srinivas, “Evolutionary methods for multidisciplinary optimization appliedto the design of UAV systems”, Engineering Optimization, Vol. 39, No. 7, pp. 773–795, October 2007.

925. Joern Mehnen, Thomas Michelitsch, Christian Lasarczyk and Thomas Bartz-Beielstein, “Multi-objective evolutionarydesign of mold temperature control using DACE for parameter optimization”, International Journal of Applied Electro-magnetics and Mechanics, Vol. 25, Nos. 1–4, pp. 661–667, 2007.

926. Pascal Cote, Lael Parrott and Robert Sabourin, “Multi-objective optimization of an ecological assembly model”, Eco-logical Informatics, Vol. 2, No. 1, pp. 23–31, January 1, 2007.

927. Maria Joao Alves and Marla Almeida, “MOTGA: A multiobjective Tchebycheff based genetic algorithm for the mul-tidimensional knapsack problem”, Computers & Operations Research, Vol. 34, No. 11, pp. 3458–3470, November2007.

928. Mario Koppen, Katrin Franke and Raul Vicente-Garcia, “Tiny GAs for image processing applications”, IEEE Compu-tational Intelligence Magazine, Vol. 1, No. 2, pp. 17–26, May 2006.

929. G. Li, M. Li, S. Azarm, J. Rambo and Y. Joshi, “Optimizing thermal design of data center cabinets with a newmulti-objective genetic algorithm”, Distributed and Parallel Databases, Vol. 21, Nos. 2–3, pp. 167–192, June 2007.

930. Chandan Guria, Mohan Varma, Surya P. Mehrotra and Santosh K. Gupta, “Simultaneous optimization of the perfor-mance of flotation circuits and their simplification using the jumping gene adaptations of genetic algorithm-II: Morecomplex problems”, International Journal of Mineral Processing, Vol. 79, No. 3, pp. 149–166, June 2006.

931. Y. Tang, P.M. Reed and J.B. Kollat, “Parallelization strategies for rapid and robust evolutionary multiobjective opti-mization in water resources applications”, Advances in Water Resources, Vol. 30, No. 3, pp. 335–353, March 2007.

932. J.B. Kollat and P.M. Reed, “A computational scaling analysis of multiobjective evolutionary algorithms in long-termgroundwater monitoring applications”, Advances in Water Resources, Vol. 30, No. 3, pp. 408–419, March 2007.

933. Ivan Blecic, Arnaldo Cecchini and Giuseppe A. Trunfio, “A decision support tool coupling a causal model and a multi-objective genetic algorithm”, Applied Intelligence, Vol. 26, No. 2, pp. 125–137, April 2007.

934. J.W. Large, D.F. Jones and M. Tamiz, “Hyper-spherical inversion transformations in multi-objective evolutionary opti-mization”, European Journal of Operational Research, Vol. 177, No. 3, pp. 1678–1702, March 16, 2007.

935. Sahnan A. Khan and Andries P. Engelbrecht, “A new fuzzy operator and its application to topology design of distributedlocal area networks”, Information Sciences, Vol. 177, No. 13, pp. 2692–2711, July 1, 2007.

936. M.R. Gholamian, S.M.T. Fatemi Ghomi and M. Ghazanfari, “A hybrid system for multiobjective problems - A casestudy in NP-hard problems”, Knowledge-Based Systems, Vol. 20, No. 4, pp. 426–436, May 2007.

937. Patrick Reed, Joshua B. Kollat and V.K. Devireddy, “Using interactive archives in evolutionary multiobjective optimiza-tion: A case study for long-term groundwater monitoring design”, Environmental Modelling & Software, Vol. 22, No. 5,pp. 683–692, May 2007.

938. Carlos Gomes da Silva, Jose Figueira and Joao Clımaco, “Integrating partial optimization with scatter search for solvingbi-criteria {0,1}-knapsack problems”, European Journal of Operational Research, Vol. 177, No. 3, pp. 1656–1677, March16, 2007.

939. M.R. Gholamian, S.M.T. Fatemi Ghomi and M. Ghazanfari, “A hybrid intelligent system for multiobjective decisionmaking problems”, Computers and Industrial Engineering, Vol. 51, No. 1, pp. 26–43, September 2006.

940. C. Gil, A. Marquez, R. Banos, M.G. Montoya and J. Gomez, “A hybrid method for solving multi-objective globaloptimization problems”, Journal of Global Optimization, Vol. 38, No. 2, pp. 265–281, June 2007.

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941. Ningchuan Xiao, David A. Bennett and Marc P. Armstrong, “Interactive evolutionary approaches to multiobjectivespatial decision making: A synthetic review”, Computers Environment and Urban Systems, Vol. 31, No. 3, pp. 232–252,May 2007.

942. Julia Handl, Douglas B. Kell and Joshua Knowles, “Multiobjective optimization in bioinformatics and computationalbiology”, IEEE-ACM Transactions on Computational Biology and Bioinformatics, Vol. 4, No. 2, pp. 279–292, April-June 2007.

943. David I. Broadhurst and Douglas B. Kell, “Statistical strategies for avoiding false discoveries in metabolomics and relatedexperiments”, Metabolomics, Vol. 2, No. 4, pp. 171–196, December 2006.

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945. S.H. Yeung, W.T. Luk, H.K. Ng, K.F. Man and C.H. Chan, “A jumping genes paradigm for the design of wide-bandpatch antenna with double shorting wall”, Microwave and Optical Technology Letters, Vol. 49, No. 3, pp. 706–709,March 2007.

946. Andres L. Medaglia, Samuel B. Graves and Jeffrey L. Ringuest, “A multiobjective evolutionary approach for linearlyconstrained project selection under uncertainty”, European Journal of Operational Research, Vol. 179, No. 3, pp.869–894, June 16, 2007.

947. Richard S. Segall and Qingyu Zhang, “Data visualization and data mining of continuous numerical and discrete nominal-valued microarray databases for bioinformatics”, Kybernetes, Vol. 34, Nos. 9–10, pp. 1538–1566, 2006.

948. Sai-Ho Yeung and Kim-Fung Man, “A jumping genes paradigm with fuzzy rules for optimizing digital IIR filters”, NeuralInformation Processing, Pt 2, Proceedings, pp. 568–577, Springer-Verlag, Lecture Notes in Computer Science Vol. 4233,2006.

949. Satish V. Ukkusuri, Tom V. Mathew and S. Travis Waller, “Robust transportation network design under demanduncertainty”, Computer-Aided Civil and Infrastructure Engineering, Vol. 22, No. 1, pp. 6–18, January 2007.

950. E. Alba, B. Dorronsoro, F. Luna, A.J. Nebro, P. Bouvry and L. Hogie, “A cellular multi-objective genetic algorithm foroptimal broadcasting strategy in metropolitan MANETs” , Computer Communications, Vol. 30, No. 4, pp. 685–697,February 26, 2007.

951. N. Lyu and K. Saitou, “Decomposition-based assembly synthesis of a three-dimensional body-in-white model for struc-tural stiffness”, Journal of Mechanical Design, Vol. 127, No. 1, pp. 34–48, January 2005.

952. L.A. Welser, R.C. Mancini, J.A. Koch, N. Izumi, H. Dalhed, H. Scott, T.W. Barbee, R.W. Lee, I.E. Golovkin, F.Marshall, J. Delettrez and L. Klein, “Analysis of the spatial structure of inertial confinement fusion implosion coresat OMEGA”, Journal of Quantitative Spectroscopy & Radiative Transfer, Inglaterra, Vol. 81, Nos. 1–4, pp. 487–497,September-November 2003.

953. W.F. Yu and K. Hidajat and A.K. Ray, “Application of multiobjective optimization in the design and operation ofreactive SMB and its experimental verification”, Industrial & Engineering Chemistry Research, Estados Unidos, Vol. 42,No. 26, pp. 6823–6831, December 24, 2003.

954. Patrick Reed, Barbara S. Minsker and David E. Goldberg, “Simplifying multiobjective optimization: An automateddesign methodology for the nondominated sorted genetic algorithm-II”, Water Resources Research, Vol. 39, No. 7, Art.No. 1196, July 30, 2003.

955. C. Guria, M. Verma, S.P. Mehrotra and S.K. Gupta, “Multi-objective optimal synthesis and design of froth flotationcircuits for mineral processing, using the jumping gene adaptation of genetic algorithm”, Industrial & EngineeringChemistry Research, Vol. 44, No. 8, pp. 2621–2633, April 13, 2005.

956. B. Suman, “Study of self-stopping PDMOSA and performance measure in multiobjective optimization”, Computers &Chemical Engineering, Vol. 29, No. 5, pp. 1131–1147, April 15, 2005.

957. V. Cotik, R.R. Zaliz and I. Zwir, “A hybrid promoter analysis methodology for prokaryotic genomes”, Fuzzy Sets andSystems, Vol. 152, No. 1, pp. 83–102, May 16, 2005.

958. K.J. Kim and R.L. Smith, “Systematic procedure for designing processes with multiple environmental objectives”,Environmental Science & Technology, Vol. 39, No. 7, pp. 2394–2405, April 1, 2005.

959. P. Di Barba, “Multiobjective design optimisation: A microeconomics-inspired strategy applied to electromagnetics”,International Journal of Applied Electromagnetics and Mechanics, Vol. 21, No. 2, pp. 101–117, 2005.

960. N. Lyu and K. Saitou, “Topology optimization of multicomponent beam structure via decomposition-based assemblysynthesis”, Journal of Mechanical Design, Vol. 127, No. 2, pp. 170–183, March 2005.

961. M.S. Osman, M.A. Abo-Sinna and M.K. El-Sayed, “An algorithm for solving multi-stage decision making model withmultiple fuzzy goals based on genetic algorithms”, International Journal of Nonlinear Sciences and Numerical Simulation,Vol. 5, No. 4, pp. 371–385, 2004.

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962. M.R. Gholamian, S.M.T.F. Ghomi and M. Ghazanfari, “A hybrid systematic design for multiobjective market problems:a case study in crude oil markets”, Engineering Applications of Artificial Intelligence, Vol. 18, No. 4, pp. 495–509, June2005.

963. A. Singh and H.H. Lou, “Hierarchical Pareto optimization for the sustainable development of industrial ecosystems”,Industrial & Engineering Chemistry Research, Vol. 45, No. 9, pp. 3265–3279, April 26, 2006.

964. K.C. Tan, Y.H. Chew and L.H. Lee, “A hybrid multiobjective evolutionary algorithm for solving vehicle routing problemwith time windows”, Computational Optimization and Applications, Vol. 34, No. 1, pp. 115–151, May 2006.

965. K.C. Tan, Y.H. Chew and L.H. Lee, “A hybrid multi-objective evolutionary algorithm for solving truck and trailervehicle routing problems”, European Journal of Operational Research, Vol. 172, No. 3, pp. 855–885, August 1st, 2006.

966. H.A. Abbass, “An economical cognitive approach for bi-objective optimization using bliss points, visualization, andinteraction”, Soft Computing, Vol. 10, No. 8, pp. 687-,698, June 2006.

967. S. Tiwari and N. Chakraborti, “Multi-objective optimization of a two-dimensional cutting problem using genetic algo-rithms”, Journal of Materials Processing Technology, Vol. 173, No. 3, pp. 384–393, April 20, 2006.

968. C. Cagne and M. Parizeau, “Genericity in evolutionary computation software tools: Principles and case-study”, Inter-national Journal on Artificial Intelligence Tools, Vol. 15, No. 2, pp. 173–194, April 2006.

969. S.L. Avila, A.C. Lisboa, L. Krahenbuhl, W.P. Carpes, J.A. Vasconcelos, R.R. Saldanha and R.H.C. Takahashi, “Sensitiv-ity analysis applied to decision making in multiobjective evolutionary optimization”, IEEE Transactions on Magnetics,Vol. 42, No. 4, pp. 1103–1106, April 2006.

970. A. Gepperth and S. Roth, “Applications of multi-objective structure optimization”, Neurocomputing, Vol. 69, Nos. 7–9,pp. 701–713, March 2006.

971. L.A. Welser, R.C. Mancini, J.A. Koch, N. Izumi, S.J. Louis, I.E. Golovkin, T.W. Barbee, S.W. Haan, J.A. Delettrez,F.J. Marshall, R.P. Regan, V.A. Smalyuk, D.A. Haynes and R.W. Lee, “Multi-objective spectroscopic analysis of coregradients: Extension from two to three objectives”, Journal of Quantitative Spectroscopy & Radiative Transfer, Vol. 99,Nos. 1–3, pp. 649–657, May-June 2006.

972. Lyndon While, Phil Hingston, Luigi Barone, and Simon Huband, “A Faster Algorithm for Calculating Hypervolume”,IEEE Transactions on Evolutionary Computation, Vol. 10, No. 1, pp. 29–38, February 2006.

973. P. Kuntz, B. Pinaud and R. Lehn, “Minimizing crossings in hierarchical digraphs with a hybridized genetic algorithm”,Journal of Heuristics, Vol. 12, Nos. 1–2, pp. 23–36, January 2006.

974. K. Foli, T. Okabe, M. Olhofer, Y.C. Jin and B. Sendhoff, “Optimization of micro heat exchanger: CFD, analyticalapproach and multi-objective evolutionary algorithms”, International Journal of Heat and Mass Transfer, Vol. 49, Nos.5–6, pp. 1090–1099, March 2006.

975. J.J. Huang, G.H. Tzeng and C.S. Ong, “Optimal fuzzy multi-criteria expansion of competence sets using multi-objectivesevolutionary algorithms”, Expert Systems with Applications, Vol. 30, No. 4, pp. 739–745, May 2005.

976. Z.V.P. Murthy and J.C. Vengal, “Optimization of a reverse osmosis system using genetic algorithm”, Separation Scienceand Technology, Vol. 41, No. 4, pp. 647–663, 2006.

977. Joshua Knowles, “ParEGO: A Hybrid Algorithm With On-Line Landscape Approximation for Expensive MultiobjectiveOptimization Problems”, IEEE Transactions on Evolutionary Computation, Vol. 10, No. 1, pp. 50–66, February 2006.

978. E.G. Talbi and H. Meunier, “Hierarchical parallel approach for GSM mobile network design”, Journal of Parallel andDistributed Computing, Vol. 66, No. 2, pp. 274–290, February 2006.

979. K.B. Matthews, K. Buchan, A.R. Sibbald and S. Craw, “Combining deliberative and computer-based methods formulti-objective land-use planning”, Agricultural Systems, Vol. 87, No. 1, pp. 18–37, January 2006.

980. F. de Toro, E. Ros, S. Mota and J. Ortega, “Evolutionary algorithms for multiobjective and multimodal optimizationof diagnostic schemes”, IEEE Transactions on Biomedical Engineering, Vol. 53, No. 2, pp. 178–189, February 2006.

981. S. Meshoul, K. Mahdi and M. Batouche, “A quantum inspired evolutionary framework for multi-objective optimization”,in Progress in Artificial Intelligence, Proceedings, pp. 190–201, Springer, Lecture Notes in Artificial Intelligence, Vol.3808, 2005.

982. B. Ombuki, B.J. Ross and F. Hanshar, “Multi-objective genetic algorithms for vehicle routing problem with timewindows”, Applied Intelligence, Vol. 24, No. 1, pp. 17–30, February 2006.

983. M.A. Panduro, C.A. Brizuela, D. Covarrubias and C. Lopez, “A trade-off curve computation for linear antenna arraysusing an evolutionary multi-objective approach”, Soft Computing, Vol. 10, No. 2, pp. 125–131, January 2006.

984. M. Liu, S.A. Burns and Y.K. Wen, “Genetic algorithm based construction-conscious minimum weight design of seismicsteel moment-resisting frames”, Journal of Structural Engineering–ASCE, Vol. 132, No. 1, pp. 50–58, January 2006.

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986. K. Deb, M. Mohan and S. Mishra, “Evaluating the epsilon-domination based multi-objective evolutionary algorithm fora quick computation of pareto-optimal solutions”, Evolutionary Computation, Vol. 13, No. 4, pp. 501–525, Winter 2005.

987. L. Poladian and L.S. Jermiin, “Multi-objective evolutionary algorithms and phylogenetic inference with multiple datasets”, Soft Computing, Vol. 10, No. 4, pp. 359–368, February 2006.

988. F. Bellas, R.J. Duro and F. Lopez-Pena, “Blind signal separation through cooperating ANNs”, Knowledge-Based In-telligent Information and Engineering Systems, Part 1, Proceedings, pp. 847–853, Springer, Lecture Notes in ArtificialIntelligence Vol. 3681, 2005.

989. Martin Trefzer, Jorg Langeheine, Karlheinz Meier and Johannes Schemmel, “Operational Amplifiers: An Example forMulti-objective Optimization on an Analog Evolvable Hardware Platform”, in J. Manuel Moreno, Jordi Madrenas andJordi Cosp (editors), Evolvable Systems: From Biology to Hardware, 6th International Conference, ICES 2005, pp.86–97, Springer, Lecture Notes in Computer Science Vol. 3637, Sitges, Spain, September 2005.

990. O. Cordon, E. Herrera-Viedma and M. Luque, “Improving the learning of Boolean queries by means of a multiobjectiveIQBE evolutionary algorithm”, Information Processing & Management, Vol. 42, No. 3, pp. 615–632, May 2006.

991. T.M. Chan, K.F. Man, K.S. Tang and S. Kwong, “A jumping gene algorithm for multiobjective resource managementin wideband CDMA systems”, Computer Journal, Vol. 48, No. 6, pp. 749–768, November 2005.

992. K.K. Kshetrapalapuram and M. Kirley, “Mining classification rules using evolutionary multi-objective algorithms”,Knowledge-Based Intelligent Information and Engineering Systems, Part 3, Proceedings, Springer, pp. 959–965, LectureNotes in Artificial Intelligence Vol. 3683, 2005.

993. T. Ray and K.W. Won, “An evolutionary algorithm for constrained bi-objective optimization using radial slots”,Knowledge-Based Intelligent Information and Engineering Systems, Part 4, Proceedings, Springer, pp. 49–56, LectureNotes in Artificial Intelligence Vol. 3684, 2005.

994. X.F. Zou and L.S. Kang, “Fast annealing genetic algorithm for multi-objective optimization problems”, InternationalJournal of Computer Mathematics, Vol. 82, No. 8, pp. 931–940, August 2005.

995. Tapio Tyni and Jari Ylinen, “Evolutionary bi-objective optimisation in the elevator car routing problem”, EuropeanJournal of Operational Research, Vol. 169, No. 3, pp. 960–977, March 16, 2006.

996. E.K. Burke and J.D. Landa Silva, “The influence of the fitness evaluation method on the performance of multiobjectivesearch algorithms”, European Journal of Operational Research, Vol. 169, No. 3, pp. 875–897, March 16, 2006.

997. K. Atashkari, N. Nariman-Zadeh, A. Pilechi, A. Jamali and X. Yao, “Thermodynamic Pareto optimization of turbojetengines using multi-objective genetic algorithms”, International Journal of Thermal Sciences, Vol. 44, No. 11, pp.1061–1071, November 2005.

998. Y.R. Zhou and J. He, “The convergence of a multi-objective evolutionary algorithm based on grids”, Advances in NaturalComputation, Pt 2, Proceedings, Springer, pp. 1015–1024, Lecture Notes in Computer Science Vol. 3611, 2005.

999. Y. Yun, M. Yoon and H. Nakayama, “Genetic algorithm for multi-objective optimization using GDEA”, Advances inNatural Computation, Pt 3, Proceedings, Springer, pp. 409–416, Lecture Notes in Computer Science Vol. 3612, 2005.

1000. C.S. Ong, H.J. Huang and G.H. Tzeng, “A novel hybrid model for portfolio selection”, Applied Mathematics andComputation, Vol. 169, No. 2, pp. 1195–1210, October 15, 2005.

1001. N. Zong and X. Hong, “Nonlinear channel equalizer design using directional evolutionary multi-objective optimization”,International Journal of Systems Science, Vol. 36, No. 12, pp. 737–755, October 10, 2005.

1002. N. Chakraborti, “Genetic algorithms in these changing steel times”, Ironmaking & Steelmaking, Vol. 32, No. 5, pp.401–404, October 2005.

1003. A. Hadi and F. Rashidi, “Design of optimal power distribution networks using multiobjective genetic algorithm”, KI2005: Advances in Artificial Intelligence, Springer, pp. 203–215, Lecture Notes in Artificial Intelligence Vol. 3698, 2005.

1004. Carlos Gomes da Silva, Joao Clımaco and Jose Figueira, “A scatter search method for bi-criteria {0,1}-knapsack prob-lems”, European Journal of Operational Research, Vol. 169, No. 2, pp. 373–391, March 1st, 2006.

1005. C. Guria, M. Verma, S.K. Gupta and S.P. Mehrotra, “Simultaneous optimization of the performance of flotation circuitsand their simplification using the jumping gene adaptations of genetic algorithm”, International Journal of MineralProcessing, Vol. 77, No. 3, pp. 165–185, November 2005.

1006. C. Guria, P.K. Bhattacharya and S.K. Gupta, “Multi-objective optimization of reverse osmosis desalination units usingdifferent adaptations of the non-dominated sorting genetic algorithm (NSGA)”, Computers & Chemical Engineering,Vol. 29, No. 9, pp. 1977–1995, August 15, 2005.

1007. A. Gaspar-Cunha and J.C. Viana, “Using multi-objective evolutionary algorithms to optimize mechanical properties ofinjection molded part”, International Polymer Processing, Vol. 20, No. 3, pp. 274–285, September 2005.

1008. Fabio Freschi and Maurizio Repetto, “Multiobjective Optimization by a Modified Artificial Immune System Algorithm”,in Christian Jacob, Marcin L. Pilat, Peter J. Bentley and Jonathan Timmis (editors), Artificial Immune Systems. 4thInternational Conference, ICARIS 2005, pp. 248–261, Springer. Lecture Notes in Computer Science Vol. 3627, Banff,Canada, August 2005.

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1009. S. Ruzika and M.M. Wiecek, “Approximation methods in multiobjective programming”, Journal of Optimization Theoryand Applications, Vol. 126, No. 3, pp. 473–501, September 2005.

1010. R. Kachhap and C. Guria, “Multi-objective optimization of a batch copoly(ethylene-polyoxyethylene terephthalate)reactor using different adaptations of nondominated sorting genetic algorithm”, Macromolecular Theory and Simulations,Vol. 14, No. 6, pp. 358–373, July 19, 2005.

1011. T. Hanne and S. Nickel, “A multiobjective evolutionary algorithm for scheduling and inspection planning in softwaredevelopment projects”, European Journal of Operational Research, Vol. 167, No. 3, pp. 663–678, December 16, 2005.

1012. S.A. Mansouri, “A Multi-Objective Genetic Algorithm for mixed-model sequencing on JIT assembly lines”, EuropeanJournal of Operational Research, Vol. 167, No. 3, pp. 696–716, December 16, 2005.

1013. Rajeev Kumar and Nilanjan Banerjee, “Running time analysis of a multiobjective evolutionary algorithm on simpleand hard problems”, in Alden H. Wright, Michael D. Vose, Kenneth A. De Jong and Lothar M. Schmitt (editors),Foundations of Genetic Algorithms. 8th International Workshop, FOGA 2005, Springer, Lecture Notes in ComputerScience Vol. 3469, pp. 112–131, Aizu-Wakamatsu City, Japan, January 2005.

1014. R. Kicinger, T. Arciszewski and K. De Jong, “Evolutionary Computation and Structural Design: A Survey of theState-of-the-art”, Computers & Structures, Vol. 83, Nos. 23–24, pp. 1943–1978, September 2005.

1015. J.M. Herrero, X. Blasco, M. Martinez and C. Ramos, “Nonlinear robust identification with epsilon-GA: FPS underseveral norms simultaneously”, in Computational Intelligence and Bioinspired Systems. Proceedings, pp. 993–1001,Springer-Verlag, Lecture Notes in Computer Science Vol. 3512, 2005.

1016. F. Bellas, J.A. Becerra and R.J. Duro, “Evolution of cooperating ANNs through functional phenotypic affinity”, inComputational Intelligence and Bioinspired Systems. Proceedings, Springer-Verlag, pp. 333–340, Lecture Notes inComputer Science Vol. 3512, 2005.

1017. M.A. Panduro, D.H. Covarrubias, C.A. Brizuela and F.R. Marante, “A multi-objective approach in the linear antennaarray design”, AEU-International Journal of Electronics and Communications, Vol. 59, No. 4, pp. 205–212, 2005.

1018. A. Gaspar-Cunha, J.A. Covas and B. Vergnes, “Defining the configuration of co-rotating twin-screw extruders withmultiobjective evolutionary algorithms”, Polymer Engineering and Science, Vol. 45, No. 8, pp. 1159–1173, August2005.

1019. M.A. Martinez, J. Sanchis and X. Blasco, “Genetic algorithms for multiobjective controller design”, in Artificial Intelli-gence and Knowledge Engineering Applications: A Bioinspired Approach. Part 2. Proceedings, Springer-Verlag, LectureNotes in Computer Science Vol. 3562, pp. 242–251, 2005.

1020. K. Rodriguez-Vazquez and P.J. Fleming, “Evolution of mathematical models of chaotic systems based on multiobjectivegenetic programming”, Knowledge and Information Systems, Vol. 8, No. 2, pp. 235–256, August 2005.

1021. N. Nariman-Zadeh, K. Atashkari, A. Jamali, A. Pilechi and X. Yao, “Inverse modelling of multi-objective thermody-namically optimized turbojet engines using GMDH-type neural networks and evolutionary algorithms”, EngineeringOptimization, Vol. 37, No. 5, pp. 437–462, July 2005.

1022. J. Balicki, “Immune systems in multi-criterion evolutionary algorithm for task assignments in distributed computersystem”, Advances in Web Intelligence, Springer, Lecture Notes in Computer Science Vol. 3528, pp. 51–56, 2005.

1023. I. Blecic, A. Cecchini and G.A. Trunfio, “A decision support tool coupling a causal model and a multi-objective geneticalgorithm”, Innovations in Applied Intelligence, Springer, Lecture Notes in Artificial Intelligence Vol. 3533, pp. 628–637,2005.

1024. O.L. Cetin and S. Saitou, “Decomposition-based assembly synthesis of multiple structures for minimum manufacturingcost”, Journal of Mechanical Design, Vol. 127, No. 4, pp. 572–579, July 2005.

1025. Y. Vidyakiran, B. Mahanty and N. Chakraborti, “A genetic-algorithms-based multiobjective approach for a three-dimensional guillotine cutting problem”, Materials and Manufacturing Processes, Vol. 20, No. 4, pp. 697–715, 2005.

1026. Yaochu Jin, Bernhard Sendhoff and Edgar Korner, “Evolutionary Multi-objective Optimization for Simultaneous Gen-eration of Signal-Type and Symbol-Type Representations”, in Carlos A. Coello Coello, Arturo Hernandez Aguirre andEckart Zitzler (editors), Evolutionary Multi-Criterion Optimization. Third International Conference, EMO 2005, pp.692–706, Springer. Lecture Notes in Computer Science Vol. 3410, Guanajuato, Mexico, March 2005.

1027. S.A. Mansouri, “Coordination of set-ups between two stages of a supply chain using multi-objective genetic algorithms”,International Journal of Production Research, Vol. 43, No. 15, pp. 3163–3180, August 1, 2005.

1028. Frank Schlottmann, Andreas Mitschele and Detlef Seese, “A Multi-objective Approach to Integrated Risk Management”,in Carlos A. Coello Coello, Arturo Hernandez Aguirre and Eckart Zitzler (editors), Evolutionary Multi-Criterion Opti-mization. Third International Conference, EMO 2005, pp. 692–706, Springer. Lecture Notes in Computer Science Vol.3410, Guanajuato, Mexico, March 2005.

1029. Hernan Aguirre and Kiyoshi Tanaka, “Selection, Drift, Recombination, and Mutation in Multiobjective EvolutionaryAlgorithms on Scalable MNK-Landscapes”, in Carlos A. Coello Coello, Arturo Hernandez Aguirre and Eckart Zitzler (ed-itors), Evolutionary Multi-Criterion Optimization. Third International Conference, EMO 2005, pp. 355–369, Springer.Lecture Notes in Computer Science Vol. 3410, Guanajuato, Mexico, March 2005.

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1030. David Greiner, Gabriel Winter, Jose M. Emperador and Blas Galvan, “Gray Coding in Evolutionary MulticriteriaOptimization: Application in Frame Structural Optimum Design”, in Carlos A. Coello Coello, Arturo Hernandez Aguirreand Eckart Zitzler (editors), Evolutionary Multi-Criterion Optimization. Third International Conference, EMO 2005,pp. 576–591, Springer. Lecture Notes in Computer Science Vol. 3410, Guanajuato, Mexico, March 2005.

1031. Juan Carlos Leyva-Lopez and Miguel Angel Aguilera-Contreras, “A Multiobjective Evolutionary Algorithm for DerivingFinal Ranking from a Fuzzy Outranking Relation”, in Carlos A. Coello Coello, Arturo Hernandez Aguirre and EckartZitzler (editors), Evolutionary Multi-Criterion Optimization. Third International Conference, EMO 2005, pp. 235–249,Springer. Lecture Notes in Computer Science Vol. 3410, Guanajuato, Mexico, March 2005.

1032. M. Laumanns and N. Laumanns, “Evolutionary multiobjective design in automotive development”, Applied Intelligence,Vol. 23, No. 1, pp. 55–70, July 2005.

1033. Jerzy Duda and Andrzej Osyczka, “Multiple Criteria Lot-Sizing in a Foundry Using Evolutionary Algorithms”, in CarlosA. Coello Coello, Arturo Hernandez Aguirre and Eckart Zitzler (editors), Evolutionary Multi-Criterion Optimization.Third International Conference, EMO 2005, pp. 651–663, Springer. Lecture Notes in Computer Science Vol. 3410,Guanajuato, Mexico, March 2005.

1034. Christian Igel, “Multi-objective Model Selection for Support Vector Machines”, in Carlos A. Coello Coello, ArturoHernandez Aguirre and Eckart Zitzler (editors), Evolutionary Multi-Criterion Optimization. Third International Con-ference, EMO 2005, pp. 443–458, Springer. Lecture Notes in Computer Science Vol. 3410, Guanajuato, Mexico, March2005.

1035. Yusuke Nojima, Kaname Narukawa, Shiori Kaige and Hisao Ishibuchi, “Effects of Removing Overlapping Solutionson the Performance of the NSGA-II Algorithm”, in Carlos A. Coello Coello, Arturo Hernandez Aguirre and EckartZitzler (editors), Evolutionary Multi-Criterion Optimization. Third International Conference, EMO 2005, pp. 341–354,Springer. Lecture Notes in Computer Science Vol. 3410, Guanajuato, Mexico, March 2005.

1036. Hisao Ishibuchi and Kaname Narukawa, “Recombination of Similar Parents in EMO Algorithms”, in Carlos A. CoelloCoello, Arturo Hernandez Aguirre and Eckart Zitzler (editors), Evolutionary Multi-Criterion Optimization. Third In-ternational Conference, EMO 2005, pp. 265–279, Springer. Lecture Notes in Computer Science Vol. 3410, Guanajuato,Mexico, March 2005.

1037. Carlos A. Brizuela and Everardo Gutierrez, “Multi-objective Go with the Winners Algorithm: A Preliminary Study”,in Carlos A. Coello Coello, Arturo Hernandez Aguirre and Eckart Zitzler (editors), Evolutionary Multi-Criterion Opti-mization. Third International Conference, EMO 2005, pp. 206–220, Springer. Lecture Notes in Computer Science Vol.3410, Guanajuato, Mexico, March 2005.

1038. Christian Haubelt, Jurgen Gamenik and Jurgen Teich, “Initial Population Construction for Convergence Improvementof MOEAs”, in Carlos A. Coello Coello, Arturo Hernandez Aguirre and Eckart Zitzler (editors), Evolutionary Multi-Criterion Optimization. Third International Conference, EMO 2005, pp. 191–205, Springer. Lecture Notes in ComputerScience Vol. 3410, Guanajuato, Mexico, March 2005.

1039. Matthieu Basseur, Franck Seynhaeve and El-Ghazali Talbi, “Path Relinking in Pareto Multi-objective Genetic Algo-rithms”, in Carlos A. Coello Coello, Arturo Hernandez Aguirre and Eckart Zitzler (editors), Evolutionary Multi-CriterionOptimization. Third International Conference, EMO 2005, pp. 120–134, Springer. Lecture Notes in Computer ScienceVol. 3410, Guanajuato, Mexico, March 2005.

1040. Adam Berry and Peter Vamplew, “The Combative Accretion Model–Multiobjective Optimisation Without ExplicitPareto Ranking”, in Carlos A. Coello Coello, Arturo Hernandez Aguirre and Eckart Zitzler (editors), EvolutionaryMulti-Criterion Optimization. Third International Conference, EMO 2005, pp. 77–91, Springer. Lecture Notes inComputer Science Vol. 3410, Guanajuato, Mexico, March 2005.

1041. Michael Emmerich, Nicola Beume and Boris Naujoks, “An EMO Algorithm Using the Hypervolume Measure as SelectionCriterion”, in Carlos A. Coello Coello, Arturo Hernandez Aguirre and Eckart Zitzler (editors), Evolutionary Multi-Criterion Optimization. Third International Conference, EMO 2005, pp. 62–76, Springer. Lecture Notes in ComputerScience Vol. 3410, Guanajuato, Mexico, March 2005.

1042. Peter Fleming, Robin C. Purshouse and Robert J. Lygoe, “Many-Objective Optimization: An Engineering DesignPerspective”, in Carlos A. Coello Coello, Arturo Hernandez Aguirre and Eckart Zitzler (editors), Evolutionary Multi-Criterion Optimization. Third International Conference, EMO 2005, pp. 14–32, Springer. Lecture Notes in ComputerScience Vol. 3410, Guanajuato, Mexico, March 2005.

1043. Rajeev Kumar, P.K. Singh and P.P. Chakrabarti, “Multiobjective EA Approach for Improved Quality of Solutions forSpanning Tree Problem”, in Carlos A. Coello Coello, Arturo Hernandez Aguirre and Eckart Zitzler (editors), EvolutionaryMulti-Criterion Optimization. Third International Conference, EMO 2005, pp. 811–825, Springer. Lecture Notes inComputer Science Vol. 3410, Guanajuato, Mexico, March 2005.

1044. Kwang Mong Sim and Bo An, “Evolving Best-Response Strategies for Market-Driven Agents Using Aggregative FitnessGA”, IEEE Transactions on Systems, Man, and Cybernetics Part C–Applications and Reviews, Vol. 39, No. 3, pp.284–298, May 2009.

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1045. R. Nandan, R. Rai, R. Jayakanth, S. Moitra, N. Chakraborti and A. Mukhopadhyay, “Regulating crown and flatness dur-ing hot rolling: A multiobjective optimization study using genetic algorithms”, Materials and Manufacturing Processes,Vol. 20, No. 3, pp. 459–478, 2005.

1046. A. Kumar, D. Sahoo, S. Chakraborty and N. Chakraborti, “Gas injection in steelmaking vessels: Coupling a fluiddynamic analysis with a genetic algorithms-based pareto-optimality”, Materials and Manufacturing Processes, Vol. 20,No. 3, pp. 363–379, 2005.

1047. C. Romero, S. Ventura and P. De Bra, “Knowledge discovery with genetic programming for providing feedback tocourseware authors”, User Modeling and User-Adapted Interaction, Vol. 14, No. 5, pp. 425–464, 2004.

1048. J. Mendoza, R. Lopez and D. Morales, “Minimal loss reconfiguration using genetic algorithms with restricted populationand addressed operators: Real application”, IEEE Transactions on Power Systems, Vol. 21, No. 2, pp. 948–954, May2006.

1049. S.H. Sun, K.F. Man, B.Z. Wang, et al., “An optimazed wideband quarter-wave patch antenna design”, IEEE Antennasand Wireless Propagation Letters, Vol. 4, pp. 486–488, 2005.

1050. J.A. Covas and A. Gaspar-Cunha, “Optimisation-based design of extruders” , Plastics Rubber and composites, Vol. 33,No. 9-10, pp. 416–425, 2004.

1051. M. Koppen, “On the benchmarking of multiobjective optimization algorithm”, Knowledge-Based Intelligent Informationand Engineering Systems, Pt 1, Proceedings, pp. 379–385, Springer, Lecture Notes in Artificial Intelligence Vol. 2773,2003.

1052. P. Kumar, D. Gospodaric and P. Bauer, “Improved genetic algorithm inspired by biological evolution”, Soft Computing,Vol. 11, No. 10, pp. 923–941, August 2007.

1053. Christian Igel, Nikolaus Hansen and Stefan Roth, “Covariance Matrix Adaptation for Multi-objective Optimization”,Evolutionary Computation, Vol. 15, No. 1, pp. 1–28, Spring 2007.

1054. S.R. Jangam and N. Chakraborti, “A novel method for alignment of two nucleic acid sequences using ant colony opti-mization and genetic algorithms”, Applied Soft Computing, Vol. 7, No. 3, pp. 1121–1130, June 2007.

1055. Martin Josef Geiger, “On operators and search space topology in multi-objective flow shop scheduling”, European Journalof Operational Research, Vol. 181, No. 1, pp. 195–206, August 16, 2007.

1056. T.M. Chan, K.F. Man, K.S. Tang and S. Kwong, “A jumping-genes paradigm for optimizing factory WLAN network”,IEEE Transactions on Industrial Informatics, Vol. 3, No. 1, pp. 33–43, February 2007.

1057. E.-G. Talbi, S. Cahon and N. Melab, “Designing cellular networks using a parallel hybrid metaheuristic on the compu-tational grid”, Computer Communications, Vol. 30, No. 4, pp. 698–713, February 26, 2007.

1058. K. Atashkari, N. Nariman-Zadeh, M. Golcu, A. Khalkhali and A. Jamali, “Modelling and multi-objective optimizationof a variable valve-timing spark-ignition engine using polynomial neural networks and evolutionary algorithms”, EnergyConversion and Management, Vol. 48, No. 3, pp. 1029–1041, March 2007.

1059. Karl Doerner, Axel Focke and Walter J. Gutjahr, “Multicriteria tour planning for mobile healthcare facilities in adeveloping country”, European Journal of Operational Research, Vol. 179, No. 3, pp. 1078–1096, June 16, 2007.

1060. Hisao Ishibuchi and Yusuke Nojima, “Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjectivefuzzy genetics-based machine learning”, International Journal of Approximate Reasoning, Vol. 44, No. 1, pp. 4–31,January 2007.

1061. L. Grandinetti, F. Guerriero, G. Lepera and M. Mancini, “A niched genetic algorithm to solve a pollutant emissionreduction problem in the manufacturing industry: A case study”, Computers & Operations Research, Vol. 34, No. 7,pp. 2191–2214, July 2007.

1062. Brian J. Ross and Eduardo Zuviria, “Evolving dynamic Bayesian networks with multi-objective genetic algorithms”,Applied Intelligence, Vol. 26, No. 1, pp. 13–23, February 2007.

1063. Lam T. Bui, Kalyanmoy Deb, Hussein A. Abbass and Daryl Essam, “Dual Guidance in Evolutionary Multi-objectiveOptimization by Localization”, Simulated Evolution and Learning, SEAL 2006, pp. 384–391, Springer, Lecture Notes inComputer Science Vol. 4247, Hefei, China, October, 2006.

1064. Miguel A. Martinez, Javier Sanchis and Xavier Blasco, “Multiobjective controller design handling human preferences”,Engineering Applications of Artificial Intelligence, Vol. 19, No. 8, pp. 927–938, December 2006.

1065. Pedro P.B. de Oliveira, Jose C. Bortot and Gina M. B. Oliveira, “The best currently known class of dynamicallyequivalent cellular automata rules for density classification”, Neurocomputing, Vol. 70, Nos. 1–3, pp. 35–43, December2006.

1066. Hisao Ishibuchi, Yusuke Nojima and Isao Kuwajima, “Finding simple fuzzy classification systems with high inter-pretability through multiobjective rule selection”, Knowledge-Based Intelligent Information and Engineering Systems,Pt 2, Proceedings, pp. 86–93, Springer, Lecture Notes in Artificial Intelligence Vol. 4252, 2006.

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1067. Thomas Hanne, “A multiobjective evolutionary algorithm for approximating the efficient set”, European Journal ofOperational Research, Vol. 176, No. 3, pp. 1723–1734, February 1, 2007.

1068. Kazi Shah Nawaz Ripon, Sam Kwong and K. F. Man, “A real-coding jumping gene genetic algorithm (RJGGA) formultiobjective optimization”, Information Sciences, Vol. 177, No. 2, pp. 632–654, January 15, 2007.

1069. Kalyanmoy Deb and Himanshu Gupta, “Introducing robustness in multi-objective optimization”, Evolutionary Compu-tation, Vol. 14, No. 4, pp. 463–494, Winter 2006.

1070. M. Ali-Tavoli, N. Nariman-Zadeh, A. Khakhali and M. Mehran, “Multi-objective optimization of abrasive flow machiningprocesses using polynomial neural networks and genetic algorithms”, Machining Science and Technology, Vol. 10, No.4, pp. 491–510, October-December 2006.

1071. F. Pettersson, N. Chakraborti and H. Saxen, “A genetic algorithms based multi-objective neural net applied to noisyblast furnace data”, Applied Soft Computing, Vol. 7, pp. 387–397, 2007.

1072. Dimo Brockhoff and Eckart Zitzler, “Are All Objectives Necessary? On Dimensionality Reduction in EvolutionaryMultiobjective Optimization”, in Thomas Philip Runarsson, Hans-Georg Beyer, Edmund Burke, Juan J. Merelo-Guervos,L. Darrell Whitley and Xin Yao (editors), Parallel Problem Solving from Nature - PPSN IX, 9th International Conference,pp. 533–542, Springer. Lecture Notes in Computer Science Vol. 4193, Reykjavik, Iceland, September 2006.

1073. Hernan E. Aguirre and Kiyoshi Tanaka, “Working principles, behavior, and performance of MOEAs on MNK-landscapes”,European Journal of Operational Research, Vol. 181, No. 3, pp. 1670–1690, 16 September, 2007.

1074. Pradyumn Kumar Shukla and Kalyanmoy Deb, “On finding multiple Pareto-optimal solutions using classical and evolu-tionary generating methods” European Journal of Operational Research, Vol. 181, No. 3, pp. 1630–1652, 16 September,2007.

1075. Hiroyuki Sato, Hernan E. Aguirre and Kiyoshi Tanaka, “Local dominance and local recombination in MOEAs on 0/1multiobjective knapsack problems”, European Journal of Operational Research, Vol. 181, No. 3, pp. 1708–1723, 16September, 2007.

1076. Julia Handl and Joshua Knowles, “An Evolutionary Approach to Multiobjective Clustering”, IEEE Transactions onEvolutionary Computation, Vol. 11, No. 1, pp. 56–76, February 2007.

1077. Francesco di Pierro, Shoon-Thiam Khu and Dragan A. Savic, “An Investigation on Preference Order Ranking Schemefor Multiobjective Evolutionary Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 11, No. 1, pp.17–45, February 2007.

1078. C. Garcıa-Martınez, O. Cordon and F. Herrera, “A taxonomy and an empirical analysis of multiple objective ant colonyoptimization algorithms for the bi-criteria TSP”, European Journal of Operational Research, Vol. 180, No. 1, pp.116–148, July 1, 2007.

1079. Darıo Maravall and Javier de Lope, “Multi-objective dynamic optimization with genetic algorithms for automatic park-ing”, Soft Computing, Vol. 11, No. 3, pp. 249–257, February 2007.

1080. J.K.L. Wong, A.J. Mason, M.J. Neve and K.W. Sowerby, “Base station placement in indoor wireless systems usingbinary integer programming”, IEE Proceedings—Communications, Vol. 153, No. 5, pp. 771–778, October 2006.

1081. L. Araujo, “Multiobjective Genetic Programming for Natural Language Parsing and Tagging”, in Thomas Philip Runars-son, Hans-Georg Beyer, Edmund Burke, Juan J. Merelo-Guervos, L. Darrell Whitley and Xin Yao (editors), ParallelProblem Solving from Nature - PPSN IX, 9th International Conference, pp. 433–442, Springer. Lecture Notes inComputer Science Vol. 4193, Reykjavik, Iceland, September 2006.

1082. P.A. Castillo, M.G. Arenas, J.J. Merelo, V.M. Rivas and G. Romero, “Multiobjective Optimization of Ensembles ofMultilayer Perceptrons for Pattern Classification”, in Thomas Philip Runarsson, Hans-Georg Beyer, Edmund Burke,Juan J. Merelo-Guervos, L. Darrell Whitley and Xin Yao (editors), Parallel Problem Solving from Nature - PPSN IX,9th International Conference, pp. 453–462, Springer. Lecture Notes in Computer Science Vol. 4193, Reykjavik, Iceland,September 2006.

1083. Mike Preuss, Boris Naujoks and Gunter Rudolph, “Pareto Set and EMOA Behavior for Simple Multimodal MultiobjectiveFunctions”, in Thomas Philip Runarsson, Hans-Georg Beyer, Edmund Burke, Juan J. Merelo-Guervos, L. Darrell Whitleyand Xin Yao (editors), Parallel Problem Solving from Nature - PPSN IX, 9th International Conference, pp. 513–522,Springer. Lecture Notes in Computer Science Vol. 4193, Reykjavik, Iceland, September 2006.

1084. Cagkan Erbas, Selin Cerac-Erbas and Andy D. Pimentel, “Multiobjective Optimization and Evolutionary Algorithmsfor the Application Mapping Problem in Multiprocessor System-on-Chip Design”, IEEE Transactions on EvolutionaryComputation, Vol. 10, No. 3, pp. 358–374, June 2006.

1085. Min Liu and Dan M. Frangopol, “Optimizing bridge network maintenance management under uncertainty with conflictingcriteria: Life-cycle maintenance, failure, and user costs”, Journal of Structural Engineering–ASCE, Vol. 132, No. 11,pp. 1835–1845, November 2006.

1086. Fabio Freschi and Maurizio Repetto, “VIS: an artificial immune network for multi-objective optimization”, EngineeringOptimization, Vol. 38, No. 8, pp. 975–996, December 2006.

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1087. B. Qian, L. Wang, D.X. Huang and X. Wang, “Multi-objective flow shop scheduling using differential evolution”,Intelligent Computing in Signal Processing and Pattern Recognition, Springer-Verlag, pp. 1125–1136, Lecture Notes inControl and Information Sciences Vol. 345, 2006.

1088. F. Luna, A.J. Nebro and E. Alba, “Observations in using Grid-enabled technologies for solving multi-objective optimiza-tion problems”, Parallel Computing, Vol. 32, Nos. 5-6, pp. 377–393, June 2006.

1089. E. Nobile, F. Pinto and G. Rizzetto, “Geometric parameterization and multiobjective shape optimization of convectiveperiodic channels”, Numerical Heat Transfer Part B–Fundamentals, Vol. 50, No. 5, pp. 425–453, November 2006.

1090. J.G. Villegas, F. Palacios and A.L. Medaglia, “Solution methods for the bi-objective (cost-coverage) unconstrainedfacility location problem with an illustrative example”, Annals of Operations Research, Vol. 147, No. 1, pp. 109–141,October 2006.

1091. D.T. Pham and M. Castellani, “Evolutionary learning of fuzzy models”, Engineering Applications of Artificial Intelli-gence, Vol. 19, No. 6, pp. 583–592, September 2006.

1092. K.C. Tan, Y.J. Yang and C.K. Goh, “A Distributed Cooperative Coevolutionary Algorithm for Multiobjective Opti-mization”, IEEE Transactions on Evolutionary Computation, Vol. 10, No. 5, pp. 527–549, October 2006.

1093. J.L. Bernal-Agustin, R. Dufo-Lopez and D.M. Rivas-Ascaso, “Design of isolated hybrid systems minimizing costs andpollutant emissions”, Renewable Energy, Vol. 31, No. 14, pp. 2227–2244, November 2006.

1094. F. Jimenez, J.M. Cadenas, G. Sanchez, A.F. Gomez-Skarmeta and J.L. Verdegay, “Multi-objective evolutionary compu-tation and fuzzy optimization”, International Journal of Approximate Reasoning, Vol. 43, No. 1, pp. 59–75, September2006.

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1098. T. Biondi, A. Ciccazzo, V. Cutello, S. D’Antona, G. Nicosia and S. Spinella, “Multi-objective evolutionary algorithmsand pattern search methods for circuit design problems”, Journal of Universal Computer Science, Vol. 12, No. 4, pp.432–449, 2006.

1099. R. Kumar and N. Banerjee, “Analysis of a Multiobjective Evolutionary Algorithm on the 0-1 knapsack problem”,Theoretical Computer Science, Vol. 358, No. 1, pp. 104–120, July 31, 2006.

1100. Y. Tang, P. Reed and T. Wagener, “How effective and efficient are multiobjective evolutionary algorithms at hydrologicmodel calibration?”, Hydrology and Earth System Sciences, Vol. 10, No. 2, pp. 289–307, 2006.

1101. J.B. Kollat and P.M. Reed, “Comparing state-of-the-art evolutionary multi-objective algorithms for long-term ground-water monitoring design”, Advances in Water Resources, Vol. 29, No. 6, pp. 792–807, June 2006.

1102. B.M. Hodge, F. Pettersson and N. Chakraborti, “Re-evaluation of the optimal operating conditions for the primary endof an integrated steel plant using multi-objective genetic algorithms and Nash equilibrium”, Steel Research International,Vol. 77, No. 7, pp. 459–461, July 2006.

1103. P. Nikitas, A. Pappa-Louisi and P. Agrafiotou, “Multilinear gradient elution optimisation in reversed-phase liquid chro-matography using genetic algorithms”, Journal of Chromatography A, Vol. 1120, Nos. 1–2, pp. 299–307, July 7, 2006.

1104. L. Siwik and M. Kisiel-Dorohinicki, “Semi-elitist evolutionary multi-agent system for multiobjective optimization”,Computational Science – ICCS 2006, Pt 3, Proceedings, pp. 831–838, Springer-Verlag, Lecture Notes in ComputerScience Vol. 3993, 2006.

1105. J. Balicki, “Negative selection with ranking procedure in tabu-based multi-criterion evolutionary algorithm for taskassignment”, Computational Science - ICCS 2006, Pt 3, Proceedings, pp. 863–870, Springer-Verlag, Lecture Notes inComputer Science Vol. 3993, 2006.

1106. N. Nariman-Zadeh, A. Darvizeh and A. Jamali, “Pareto optimization of energy absorption of square aluminium columnsusing multi-objective genetic algorithms”, Proceedings of the Institution of Mechanical Engineers Part B–Journal ofEngineering Manufacture, Vol. 220, No. 2, pp. 213–224, February 2006.

1107. P. Lacomme, C. Prins and M. Sevaux, “A genetic algorithm for a bi-objective capacitated arc routing problem”, Com-puters & Operations Research, Vol. 33, No. 12, pp. 3473–3493, December 2006.

1108. H.S. Kim and P.N. Roschke, “Fuzzy control of base-isolation system using multi-objective genetic algorithm”, Computer-Aided Civil and Infrastructure Engineering, Vol. 21, No. 6, pp. 436–449, August 2006.

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1109. R. Romero-Zaliz, C. Rubio-Escudero, O. Cordon, O. Harari, C. del Val and I. Zwir, “Mining structural databases:An evolutionary multi-objetive conceptual clustering methodology”, in Applications of Evolutionary Computing, pp.159–171, Springer, Lecture Notes in Computer Science Vol. 3907, 2006.

1110. Giuseppe Ascia, Vincenzo Catania and Maurizio Palesi, “A multi-objective genetic approach to mapping problem onNetwork-on-Chip”, Journal of Universal Computer Science, Vol. 12, No. 4, pp. 370–394, 2006.

1111. M.A. Elsays, M. Naguib Aly and A.A. Badawi, “Design optimization of shell-and-tube heat exchangers using singleobjective and multiobjective particle swarm optimization”, Kerntechnik, Vol. 75, Nos. 1–2, pp. 38–46, March 2010.

1112. Pedro G. Espejo, Sebastian Ventura and Francisco Herrera, “A Survey on the Application of Genetic Programming toClassification”, IEEE Transactions on Systems, Man, and Cybernetics Part C–Applications and Reviews, Vol. 40, No.2, pp. 121–144, March 2010.

1113. Hans Ole Rafaelsen, Frank Eliassen and Sharath Babu Musunoori, “Towards self-organizing distribution structures forstreaming media”, in R. Meersman and Z. Tari (editors), On the Move to Meaningful Internet Systems 2006: COOPIS,DOA, GADA, and ODBASE, pp. 1825–1842, Springer, Lecture Notes in Computer Science Vol. 4276, 2006.

1114. Shin Yoo and Mark Harman, “Using hybrid algorithm for Pareto efficient multi-objective test suite minimisation”,Journal of Systems and Software, Vol. 83, No. 4, pp. 689–701, April 2010.

1115. Anna Syberfeldt, Amos Ng, Robert I. John and Philip Moore, “Evolutionary optimisation of noisy multi-objectiveproblems using confidence-based dynamic resampling”, European Journal of Operational Research, Vol. 204, No. 3, pp.533–544, August 1, 2010.

1116. Tobias Friedrich, Nils Hebbinghaus and Frank Neumann, “Plateaus can be harder in multi-objective optimization”,Theoretical Computer Science, Vol. 411, No. 6, pp. 854–864, February 6, 2010.

1117. Sonda Elloumi and Philippe Fortemps, “A hybrid rank-based evolutionary algorithm applied to multi-mode resource-constrained project scheduling problem”, European Journal of Operational Research, Vol. 205, No. 1, pp. 31–41, August16, 2010.

1118. Rajeev Kumar and P.K. Singh, “Assessing solution quality of biobjective 0-1 knapsack problem using evolutionary andheuristic algorithms”, Applied Soft Computing, Vol. 10, No. 3, pp. 711–718, June 2010.

1119. Manojkumar Ramteke and Santosh K. Gupta, “Biomimetic Adaptation of the Evolutionary Algorithm, NSGA-II-aJG,Using the Biogenetic Law of Embryology for Intelligent Optimization”, Industrial & Engineering Chemistry Research,Vol. 48, No. 17, pp. 8054–8067, September 2, 2009.

1120. Michael Dellnitz, Sina Ober-Blobaum, Marcus Post, Oliver Schutze and Bianca Thiere, “A multi-objective approach tothe design of low thrust space trajectories using optimal control”, Celestial Mechanics & Dynamical Astronomy, Vol.105, Nos. 1–3, pp. 33–59, November 2009.

1121. Jessica A. Carballido, Ignacio Ponzoni and Nelida B. Brignole, “SID-GA: An evolutionary approach for improvingobservability and redundancy analysis in structural instrumentation design”, Computers & Industrial Engineering, Vol.56, No. 4, pp. 1419–1428, May 2009.

1122. X. B. Lam, Y.S. Kim, A.D. Hoang and C.W. Park, “Coupled Aerostructural Design Optimization Using the KrigingModel and Integrated Multiobjective Optimization Algorithm”, Journal of Optimization Theory and Applications, Vol.142, No. 3, pp. 533–556, September 2009.

1123. Robert D. Clark and Edmond Abrahamian, “Using a staged multi-objective optimization approach to find selectivepharmacophore models”, Journal of Computer-Aided Molecular Design, Vol. 23, No. 11, pp. 765–771, November 2009.

1124. G. Nildem Demir, A. Sima Uyar and Sule Gunduz-Oguducu, “Multiobjective evolutionary clustering of Web user sessions:a case study in Web page recommendation”, Soft Computing, Vol. 14, No. 6, pp. 579–597, April 2010.

1125. K.H. Gudmundsson, F. Jonsdottir and F. Thorsteinsson, “A geometrical optimization of a magneto-rheological rotarybrake in a prosthetic knee”, Smart Materials & Structures, Vol. 19, No. 3, Article Number: 035023, March 2010.

1126. S.H. Yeung and K.F. Man, “Narrow Band-Stop Filters Design with I-Shape Resonators”, Microwave and Optical Tech-nology Letters, Vol. 52, No. 3, pp. 757–763, March 2010.

1127. P. Rocca, M. Benedetti, M. Donelli, D. Franceschini and A. Massa, “Evolutionary optimization as applied to inversescattering problems”, Inverse Problems, Vol. 25, No. 12, Article Number: 123003, December 2009.

1128. Manojkumar Ramteke and Santosh K. Gupta, “Biomimicking Altruistic Behavior of Honey Bees in Multi-objectiveGenetic Algorithm”, Industrial & Engineering Chemistry Research, Vol. 48, No. 21, pp. 9671–9685, November 4, 2009.

1129. Ujjwal Maulik, Anirban Mukhopadhyay and Sanghamitra Bandyopadhyay, “Finding Multiple Coherent Biclusters inMicroarray Data Using Variable String Length Multiobjective Genetic Algorithm”, IEEE Transactions on InformationTechnology in Biomedicine, Vol. 13, No. 6, pp. 969–975, November 2009.

1130. M.H. Kobayashi, H-T. C. Pedro, R.M. Kolonay and G.W. Reich, “On a cellular division method for aircraft structuraldesign”, Aeronautical Journal, Vol. 113, No. 1150, pp. 821–831, December 2009.

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1131. David Daum and Nicolas Morel, “Identifying important state variables for a blind controller”, Building and Environment,Vol. 45, No. 4, pp. 887–900, April 2010.

1132. Chung Min Kwan and C.S. Chang, “Timetable synchronization of mass rapid transit system using multiobjective evo-lutionary approach”, IEEE Transactions on Systems, Man, and Cybernetics Part C–Applications and Reviews, Vol. 38,No. 5, pp. 636–648, September 2008.

1133. S. Ganguly, S. Datta, P.P. Chattopadhyay and N. Chakraborti, “Designing the Multiphase Microstructure of Steel forOptimal TRIP Effect: A Multiobjective Genetic Algorithm Based Approach”, Materials and Manufacturing Processes,Vol. 24, No. 1, pp. 31–37, 2009.

1134. Arijit Biswas, N. Chakraborti and P.K. Sen, “Multiobjective Optimization of Manganese Recovery from Sea NodulesUsing Genetic Algorithms”, Materials and Manufacturing Processes, Vol. 24, No. 1, pp. 22–30, 2009.

1135. Nicolas Jozefowiez, Frederic Semet and El-Ghazali Talbi, “An evolutionary algorithm for the vehicle routing problemwith route balancing”, European Journal of Operational Research, Vol. 195, No. 3, pp. 761–769, June 16, 2009.

1136. Lino J. Alvarez-Vazquez, Eva Balsa-Canto and Aurea Martinez, “Optimal design and operation of a wastewater purifi-cation system”, Mathematics and Computers in Simulation, Vol. 79, No. 3, pp. 668–682, December 1, 2008.

1137. A. Rama Mohan Rao and P.P. Shyju, “A Meta-Heuristic Algorithm for Multi-Objective Optimal Design of HybridLaminate Composite Structures”, Computer-Aided Civil and Infrastructure Engineering, Vol. 25, No. 3, pp. 149–170,April 2010.

1138. G. Nildem Demir, A. Sima Uyar and Sule Gunduz-Oguducu, “Multiobjective evolutionary clustering of Web user sessions:a case study in Web page recommendation”, Soft Computing - A Fusion of Foundations, Methodologies and Applications,Vol. 14, No. 6, pp. 579–597, January, 2010.

1139. Kaushik Suresh, Debarati Kundu, Sayan Ghosh, Swagatam Das and Ajith Abraham, “Data Clustering Using Multi-objective Differential Evolution Algorithms”, Fundamenta Informaticae, Vol. 97, No. 4, pp. 381–403, 2009.

1140. Kaushik Suresh, Debarati Kundu, Sayan Ghosh, Swagatam Das, Ajith Abraham and Sang Yong Han, “Multi-ObjectiveDifferential Evolution for Automatic Clustering with Application to Micro-Array Data Analysis”, Sensors, Vol. 9, No.5, pp. 3981–4004, May 2009.

1141. Silvia Curteanu and Maria Cazacu, “Optimization of a Polysiloxane Synthesis Process using Artificial Intelligence Meth-ods”, Revue Roumaine de Chimie, Vol. 53, No. 12, pp. 1141–1148, December 2008.

1142. Zhanpeng Jin and Allen C. Cheng, “Evolutionary Benchmark Subsetting”, IEEE Micro, Vol. 28, No. 6, pp. 20–36,November-December 2008.

1143. Zhen Gao, Dan Zhang and Yunjian Ge, “Design optimization of a spatial six degree-of-freedom parallel manipulatorbased on artificial intelligence approaches”, Robotics and Computer-Integrated Manufacturing, Vol. 26, No. 2, pp.180–189, April 2010.

1144. Luis Gerardo de la Fraga and Oliver Schutze, “Direct Calibration by Fitting of Cuboids to a Single Image UsingDifferential Evolution”, International Journal of Computer Vision, Vol. 81, No. 2, pp. 119–127, February 2009.

1145. Chris Thachuk, Jose Crossa, Jorge Franco, Susanne Dreisigacker, Marilyn Warburton and Guy F. Davenport, “CoreHunter: an algorithm for sampling genetic resources based on multiple genetic measures”, BMC Bioinformatics, Vol.10, Article Number 243, August 6, 2009.

1146. Babak Forouraghi, “Optimal tolerance allocation using a multiobjective particle swarm optimizer”, International Journalof Advanced Manufacturing Technology, Vol. 44, Nos. 7–8, pp. 710–724, October 2009.

1147. Yusuke Nojima, Hisao Ishibuchi and Isao Kuwajima, “Parallel distributed genetic fuzzy rule selection”, Soft Computing,Vol. 13, No. 5, pp. 511–519, March 2009.

1148. A.F. Carazo, Trinidad Gomez, Julian Molina, Alfredo G. Hernandez-Diaz, Flor M. Guerrero and Rafael Caballero,“Solving a comprehensive model for multiobjective project portfolio selection”, Computers & Operations Research, Vol.37, No. 4, pp. 630–639, April 2010.

1149. Eduardo Fernandez, Jorge Navarro and Sergio Bernal, “Handling multicriteria preferences in cluster analysis”, EuropeanJournal of Operational Research, Vol. 202, No. 3, pp. 819–827, May 1, 2010.

1150. Leila Dridi, Alain Mailhot, Marc Parizeau and Jean-Pierre Villeneuve, “Multiobjective Approach for Pipe ReplacementBased on Bayesian Inference of Break Model Parameters”, Journal of Water Resources Planning and Management–ASCE, Vol. 135, No. 5, pp. 344–354, September-October 2009.

1151. Francisco Martinez-Lopez and Jorge Casillas, “Marketing Intelligent Systems for consumer behaviour modelling by adescriptive induction approach based on Genetic Fuzzy Systems”, Industrial Marketing Management, Vol. 38, No. 7,pp. 714–731, October 2009.

1152. David Greiner, Juan J. Aznarez, Orlando Maeso and Gabriel Winter, “Single- and multi-objective shape design of Y-noise barriers using evolutionary computation and boundary elements”, Advances in Engineering Software, Vol. 41, No.2, pp. 368–378, February 2010.

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1153. Axel Soto, Rocio L. Cecchini, Gustavo E. Vazquez and Ignacio Ponzoni, “Multi-Objective Feature Selection in QSARUsing a Machine Learning Approach”, QSAR & Combinatorial Science, Vol. 28, Nos. 11–12, pp. 1509–1523, December2009.

1154. Kishalay Mitra, “Multiobjective optimization of an industrial grinding operation under uncertainty”, Chemical Engi-neering Science, Vol. 64, No. 23, pp. 5043–5056, December 1, 2009.

1155. J.E. Mendoza, L.A. Villaleiva, M.A. Castro and E.A. Lopez, “Multi-objective Evolutionary Algorithms for Decision-Making in Reconfiguration Problems Applied to the Electric Distribution Networks”, Studies in Informatics and Control,Vol. 18, No. 4, pp. 325–336, December 2009.

1156. A. Liefooghe, L. Jourdan and E.-G. Talbi, “Metaheuristics and cooperative approaches for the Bi-objective Ring StarProblem”, Computers & Operations Research, Vol. 37, No. 6, pp. 1033–1044, June 2010.

1157. Jeffrey S. Parker and George H. Born, “Direct Lunar Halo Orbit Transfers”, Journal of the Astronautical Sciences, Vol.56, No. 4, pp. 441–476, October-December 2008.

1158. K.P. Anagnostopoulos and G. Mamanis, “A portfolio optimization model with three objectives and discrete variables”,Computers & Operations Research, Vol. 37, No. 7, pp. 1285–1297, July 2010.

1159. Nicola Beume, “S-Metric Calculation by Considering Dominated Hypervolume as Klee’s Measure Problem”, EvolutionaryComputation, Vol. 17, No. 4, pp. 477–492, Winter 2009.

1160. J.R. Kasprzyk, P.M. Reed, B.R. Kirsch and G.W. Characklis, “Managing population and drought risks using many-objective water portfolio planning under uncertainty”, Water Resources Research, Vol. 45, Article Number: W12401,December 3, 2009.

1161. M.H. Khoshgoftar Manesh and Majid Amidpour, “Multi-objective thermoeconomic optimization of coupling MSF desali-nation with PWR nuclear power plant through evolutionary algorithms”, Desalination, Vol. 249, No. 3, pp. 1332–1344,December 25, 2009.

1162. Jacek Zak, Andrzej Jaszkiewicz and Adam Redmer, “Multiple Criteria Optimization Method for the Vehicle AssignmentProblem in a Bus Transportation Company”, Journal of Advanced Transportation, Vol. 43, No. 2, pp. 203–243, 2009.

1163. J.A. Covas and A. Gaspar-Cunha, “Extrusion Scale-up: An Optimization-based Methodology”, International PolymerProcessing, Vol. 24, No. 1, pp. 67–82, March 2009.

1164. Arijit Biswas, N. Chakraborti and P.K. Sen, “A Genetic Algorithms Based Multi-Objective Optimization ApproachApplied to a Hydrometallurgical Circuit for Ocean Nodules”, Mineral Processing and Extractive Metallurgy Review, Vol.30, No. 2, pp. 163–189, 2009.

1165. A.M. Mora, J.J. Merelo, J.L.J. Laredo, C. Millan and J. Torrecillas, “CHAC, A MOACO Algorithm for Computation ofBi-Criteria Military Unit Path in the Battlefield: Presentation and First Results”, International Journal of IntelligentSystems, Vol. 24, No. 7, pp. 818–843, July 2009.

1166. A. Jamali, A. Hajiloo and N. Nariman-zadeh, “Reliability-based robust Pareto design of linear state feedback controllersusing a multi-objective uniform-diversity genetic algorithm (MUGA)”, Expert Systems with Applications, Vol. 37, No.1, pp. 401–413, January 2010.

1167. Hamidreza Eskandari and Christopher D. Geiger, “Evolutionary multiobjective optimization in noisy problem environ-ments”, Journal of Heuristics, Vol. 15, No. 6, pp. 559–595, December 2009.

1168. Jawed Iqbal and Chandan Guria, “Optimization of an operating domestic wastewater treatment plant using elitist non-dominated sorting genetic algorithm”, Chemical Engineering Research & Design, Vol. 87, No. 11A, pp. 1481–1496,November 2009.

1169. Juliane Muller, “Approximative solutions to the bicriterion Vehicle Routing Problem with Time Windows”, EuropeanJournal of Operational Research, Vol. 202, No. 1, pp. 223–231, April 1, 2010.

1170. Kostas Florios, George Mavrotas and Danae Diakoulaki, “Solving multiobjective, multiconstraint knapsack problemsusing mathematical programming and evolutionary algorithms”, European Journal of Operational Research, Vol. 203,No. 1, pp. 14–21, May 16, 2010.

1171. Anirban Dhar and Bithin Datta, “Saltwater Intrusion Management of Coastal Aquifers. I: Linked Simulation-Optimization”,Journal of Hydrologic Engineering, Vol. 14, No. 12, pp. 1263–1272, December 2009.

1172. Nicola Beume, Boris Naujoks and Guenter Rudolph, “SMS-EMOA - Effective Evolutionary Multiobjective Optimiza-tion”, AT-Automatisierungstechnik, Vol. 56, No. 7, pp. 357–364, 2008.

1173. M. Pouraghaie, K. Atashkari, S.M. Besarati and N. Nariman-Zadeh, “Thermodynamic performance optimization of acombined power/cooling cycle”, Energy Conversion and Management, Vol. 51, No. 1, pp. 204–211, January 2010.

1174. Anthony Chen, Juyoung Kim, Seungjae Lee and Youngchan Kim, “Stochastic multi-objective models for network designproblem”, Expert Systems with Applications, Vol. 37, No. 2, pp. 1608–1619, March 2010.

1175. Ioannis C. Kampolis and Kyriakos C. Giannakoglou, “Distributed evolutionary algorithms with hierarchical evaluation”,Engineering Optimization, Vol. 41, No. 11, pp. 1037–1049, November 2009.

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1176. Patrick M. Reed, Joshua B. Kollat, Matthew P. Ferringer and Timothy G. Thompson, “Parallel Evolutionary Multi-Objective Optimization on Large, Heterogeneous Clusters: An Applications Perspective”, Journal of Aerospace Com-puting Information and Communication, Vol. 5, No. 11, pp. 460–478, 2008.

1177. N. Chakraborti, S. Moitra, A. Mitra and A. Mukhopadhyay, “Evolutionary and genetic algorithms applied to hot rolling:A multi-objective rolling schedule studied using particle swarm algorithm”, Transactions of the Indian Institute of Metals,Vol. 59, No. 5, pp. 681–688, October 2006.

1178. Yujia Wang and Yupu Yang, “Particle swarm optimization with preference order ranking for multi-objective optimiza-tion”, Information Sciences, Vol. 179, No. 12, pp. 1944–1959, May 30, 2009.

1179. Yujia Wang and Yupu Yang, “Particle swarm with equilibrium strategy of selection for multi-objective optimization”,European Journal of Operational Research, Vol. 200, No. 1, pp. 187–197, January 1, 2010.

1180. Baidurya Bhattacharya, G.R. Dinesh Kumar, Akash Agarwal, Sakir Erkoc, Arunima Singh and Nirupam Chakraborti,“Analyzing Fe-Zn system using molecular dynamics, evolutionary neural nets and multi-objective genetic algorithms”,Computational Materials Science, Vol. 46, No. 4, pp. 821–827, October 2009.

1181. Aimin Zhou, Qingfu Zhang and Yaochu Jin, “Approximating the Set of Pareto-Optimal Solutions in Both the Decisionand Objective Spaces by an Estimation of Distribution Algorithm”, IEEE Transactions on Evolutionary Computation,Vol. 13, No. 5, pp. 1167–1189, October 2009.

1182. Gisele L. Pappa and Alex A. Freitas, “Evolving rule induction algorithms with multi-objective grammar-based geneticprogramming”, Knowledge and Information Systems, Vol. 19, No. 3, pp. 283–309, June 2009.

1183. Nicola Beume, Carlos M. Fonseca, Manuel Lopez-Ibanez, Luis Paquete and Jan Vahrenhold, “On the Complexity ofComputing the Hypervolume Indicator”, IEEE Transactions on Evolutionary Computation, Vol. 13, No. 5, pp. 1075–1082, October 2009.

1184. Anthony Finkelstein, Mark Harman, S. Afshin Mansouri, Jian Ren, Yuanyuan Zhang, “A search based approach to fair-ness analysis in requirement assignments to aid negotiation, mediation and decision making”, Requirements Engineering,Vol. 14, No. 4, pp. 231–245, December 2009.

1185. A. Rama Mohan Rao and K. Lakshmi, “Multi-objective Optimal Design of Hybrid Laminate Composite Structures UsingScatter Search”, Journal of Composite Materials, Vol. 43, No. 20, pp. 2157–2182, September 2009.

1186. Mario Camara, Julio Ortega and Francisco de Toro, “A single front genetic algorithm for parallel multi-objective opti-mization in dynamic environments”, Neurocomputing, Vol. 72, Nos. 16–18, pp. 3570–3579, October 2009.

1187. Parames Chutima and Penpak Pinkoompee, “Multi-objective sequencing problems of mixed-model assembly systemsusing memetic algorithms”, Scienceasia, Vol. 35, No. 3, pp. 295–305, September 2009.

1188. Lam T. Bui, Hussein A. Abbass and Daryl Essam, “Localization for Solving Noisy Multi-Objective Optimization Prob-lems”, Evolutionary Computation, Vol. 17, No. 3, pp. 379–409, Fall 2009.

1189. Aimin Zhou, Qingfu Zhang and Yaochu Jin, “Approximating the Set of Pareto-Optimal Solutions in Both the Decisionand Objective Spaces by an Estimation of Distribution Algorithm”, IEEE Transactions on Evolutionary Computation,Vol. 13, No. 5, pp. 1167–1189, October 2009.

1190. Nicola Beume, Carlos M. Fonseca, Manuel Lopez-Ibanez, Luis Paquete and Jan Vahrenhold, “On the Complexity ofComputing the Hypervolume Indicator”, IEEE Transactions on Evolutionary Computation, Vol. 13, No. 5, pp. 1075–1082, October 2009.

1191. Jiaquan Gao and Jun Wang, “WBMOAIS: A novel artificial immune system for multiobjective optimization”, Computers& Operations Research, Vol. 37, No. 1, pp. 50–61, January 2010.

1192. Rafael Alcala, Pietro Ducange, Francisco Herrera, Beatrice Lazzerini and Francesco Marcelloni, “A MultiobjectiveEvolutionary Approach to Concurrently Learn Rule and Data Bases of Linguistic Fuzzy-Rule-Based Systems”, IEEETransactions on Fuzzy Systems, Vol. 17, No. 5, pp. 1106–1122, October 2009.

1193. Anthony Chen, Kitti Subprasom and Zhaowang Ji, “A simulation-based multi-objective genetic algorithm (SMOGA)procedure for BOT network design problem”, Optimization and Engineering, Vol. 7, No. 3, pp. 225–247, September2006.

1194. Yang Zhang and Peter Rockett, “A Comparison of three evolutionary strategies for multiobjective genetic programming”,Artificial Intelligence Review, Vol. 27, Nos. 2–3, pp. 149–163, March 2007.

1195. Manojkumar Ramteke and Santosh K. Gupta, “Multiobjective Optimization of an Industrial Nylon-6 Semi Batch ReactorUsing the a-Jumping Gene Adaptations of Genetic Algorithm and Simulated Annealing”, Polymer Engineering andScience, Vol. 48, No. 11, pp. 2198–2215, November 2008.

1196. Andrzej Jaszkiewicz and Piotr Zielniewicz, “Pareto memetic algorithm with path relinking for bi-objective travelingsalesperson problem”, European Journal of Operational Research, Vol. 193, No. 3, pp. 885–890, March 16, 2009.

1197. Jose L. Ceciliano Meza, Mehmet Bayram Yildirim and Abu S.M. Masud, “A Multiobjective Evolutionary ProgrammingAlgorithm and Its Applications to Power Generation Expansion Planning”, IEEE Transactions on Systems, Man, andCybernetics, Part A–Systems and Humans, Vol. 39, No. 5, pp. 1086–1096, September 2009.

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1198. Hai-Lin Liu, Yuping Wang and Yiu-Ming Cheung, “A Multi-Objective Evolutionary Algorithm using Min-Max Strategyand Sphere Coordinate Transformation”, Intelligent Automation and Soft Computing, Vol. 15, No. 3, pp. 361–384,2009.

1199. Hussein A. Abbass, Sameer Alam and Axel Bender, “MEBRA: Multiobjective Evolutionary-Based Risk Assessment”,IEEE Computational Intelligence Magazine, Vol. 4, No. 3, pp. 29–36, August 2009.

1200. K.F. Doerner, W.J. Gutjahr, R.F. Hartl, C. Strauss and C. Stummer, “Nature-inspired metaheuristics for multiobjectiveactivity crashing”, Omega–International Journal of Management Science, Vol. 36, No. 6, pp. 1019–1037, December2008.

1201. Petra Kersting and Andreas Zabel, “Optimizing NC-tool paths for simultaneous five-axis milling based on multi-population multi-objective evolutionary algorithms”, Advances in Engineering Software, Vol. 40, No. 6, pp. 452–463,June 2009.

1202. A. Jamali, N. Nariman-zadeh, A. Darvizeh, A. Masoumi and S. Hamrang, “Multi-objective evolutionary optimizationof polynomial neural networks for modelling and prediction of explosive cutting process”, Engineering Applications ofArtificial Intelligence, Vol. 22, Nos. 4-5, pp. 676–687, June 2009.

1203. Vijay Pratap Singh, Bertrand Duquet, Michel Leger and Marc Schoenauer, “Automatic wave-equation migration velocityinversion using multiobjective evolutionary algorithms”, Geophysics, Vol. 73, No. 5, pp. 61–73, September-October 2008.

1204. Jose L. Bernal-Agustin and Rodolfo Dufo-Lopez, “Multi-objective design and control of hybrid systems minimizing costsand unmet load”, Electric Power Systems Research, Vol. 79, No. 1, pp. 170–180, January 2009.

1205. Christos Baloukas, Jose L. Risco-Martin, David Atienza, Christophe Poucet, Lazaros Papadopoulos, Stylianos Mam-agkakis, Dimitrios Soudris, J. Ignacio Hidalgo, Francky Catthoor and Juan Lanchares, “Optimization methodologyof dynamic data structures based on genetic algorithms for multimedia embedded systems”, Journal of Systems andSoftware, Vol. 82, No. 4, pp. 590–602, April 2009.

1206. Wei Wei, Yixiong Feng, Jianrong Tan and Zhongkai Li, “Product platform two-stage quality optimization design basedon multiobjective genetic algorithm”, Computers & Mathematics with Applications, Vol. 57, Nos. 11–12, pp. 1929–1937,June 2009.

1207. Leila Dridi, Marc Parizeau, Alain Mailhot and Jean-Pierre Villeneuve, “Using evolutionary optimization techniques forscheduling water pipe renewal considering a short planning horizon”, Computer-Aided Civil and Infrastructure Engineer-ing, Vol. 23, No. 8, pp. 625–635, November 2008.

1208. David L. Overbye, “The Influence of Darwin on Evolutionary Algorithms from ”Dinner with Darwin””, American BiologyTeacher, Vol. 71, No. 2, pp. 81–83, February 2009.

1209. Ata Allah Taleizadeh, Seyed Taghi Akhavan Niaki and Mir-Bahador Aryanezhad, “A hybrid method of Pareto, TOP-SIS and genetic algorithm to optimize multi-product multi-constraint inventory control systems with random fuzzyreplenishments”, Mathematical and Computer Modelling, Vol. 49, Nos. 5-6, pp. 1044–1057, March 2009.

1210. S. Afshin Mansouri, S. Hamed Hendizadeh and Nasser Salmasi, “Bicriteria scheduling of a two-machine flowshop withsequence-dependent setup times”, International Journal of Advanced Manufacturing Technology, Vol. 40, Nos. 11–12,pp. 1216–1226, February 2009.

1211. Mohammed Shalaby and Kazuhiro Saitou, “High-Stiffness, Lock-and-Key Heat-Reversible Locator-Snap Systems for theDesign for Disassembly”, Journal of Mechanical Design, Vol. 131, No. 4, Article Number: 041005, April 2009.

1212. Matteo Nicolini and Luigino Zovatto, “Optimal Location and Control of Pressure Reducing Valves in Water Networks”,Journal of Water Resources Planning and Management–ASCE, Vol. 135, No. 3, pp. 178–187, May-June 2009.

1213. Mohammed M. Shalaby, Zhongde Wang, Linda L-W. Chow, Brian D. Jensen, John L. Volakis, Katsuo Kurabayashi andKazuhiro Saitou, “Robust Design of RF-MEMS Cantilever Switches Using Contact Physics Modeling”, IEEE Transac-tions on Industrial Electronics, Vol. 56, No. 4, pp. 1012–1021, April 2009.

1214. Y. Shi and R.D. Reitz, “Optimization study of the effects of bowl geometry, spray targeting, and swirl ratio for aheavy-duty diesel engine operated at low and high load”, International Journal of Engine Research, Vol. 9, No. 4, pp.325–346, August 2008.

1215. Alexandre M. Baltar and Darrell G. Fontane, “Use of multiobjective particle swarm optimization in water resourcesmanagement”, Journal of Water Resources Planning and Management–ASCE, Vol. 134, No. 3, pp. 257–265, May-June2008.

1216. M.A. Elsays, M. Naguib Aly and A.A. Badawi, “Optimizing the dynamic response of the H. B. Robinson nuclear plantusing multiobjective particle swarm optimization”, Kerntechnik, Vol. 74, Nos. 1–2, pp. 70–78, April 2009.

1217. Rodolfo Dufo-Lopez and Jose L. Bernal-Agustin, “Multi-objective design of PV-wind-diesel-hydrogen-battery systems”,Renewable Energy, Vol. 33, No. 12, pp. 2559–2572, December 2008.

1218. Asish Kumar Sharma, Chandramouli Kulshreshtha and Kee-Sun Sohn, “Discovery of New Green Phosphors and Min-imization of Experimental Inconsistency Using a Multi-Objective Genetic Algorithm-Assisted Combinatorial Method”,Advanced Functional Materials, Vol. 19, No. 11, pp. 1705–1712, June 9, 2009.

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1219. Franklin Mendoza, Jose L. Bernal-Agustin and Jose A. Dominguez-Navarro, “NSGA and SPEA applied to multiobjectivedesign of power distribution systems”, IEEE Transactions on Power Systems, Vol. 21, No. 4, pp. 1938–1945, November2006.

1220. G.N. Beligiannis, C. Moschopoulos, S.D. Likothanassis, “A genetic algorithm approach to school timetabling”, Journalof the Operational Research Society, Vol. 60, No. 1, pp. 23–42, January 2009.

1221. Benjamin Torben-Nielsen and Klaus M. Stiefel, “Systematic mapping between dendritic function and structure”, Network-Computation in Neural Systems, Vol. 20, No. 2, pp. 59–105, 2009.

1222. J. Branke, B. Scheckenbach, M. Stein, K. Deb and H. Schmeck, “Portfolio optimization with an envelope-based multi-objective evolutionary algorithm”, European Journal of Operational Research, Vol. 199, No. 3, pp. 684–693, December16, 2009.

1223. A.G. Lopez-Herrera, E. Herrera-Viedma and F. Herrera, “Applying multi-objective evolutionary algorithms to the au-tomatic learning of extended Boolean queries in fuzzy ordinal linguistic information retrieval systems”, Fuzzy Sets andSystems, Vol. 160, No. 15, pp. 2192–2205, August 1, 2009.

1224. Antonio Nebro, Juan J. Durillo, Francisco Luna, Bernabe Dorronsoro and Enrique Alba, “MOCell: A Cellular GeneticAlgorithm for Multiobjective Optimization”, International Journal of Intelligent Systems, Vol. 24, No. 7, pp. 726–746,July 2009.

1225. Ricardo Brunelli and Christian von Lucken, “Optimal Crop Selection Using Multiobjective Evolutionary Algorithms”,AI Magazine, Vol. 30, No. 2, pp. 96–105, Summer 2009.

1226. Brahim Aghezzaf and Mohamed Naimi, “The two-stage recombination operator and its application to the multiobjective0/1 knapsack problem: A comparative study”, Computers & Operations Research, Vol. 36, No. 12, pp. 3247–3262,December 2009.

1227. J.M. Herrero, S. Garcia-Nieto, X. Blasco, V. Romero-Garcia, J.V. Sanchez-Perez, L.M. Garcia-Raffi, “Optimization ofsonic crystal attenuation properties by ev-MOGA multiobjective evolutionary algorithm”, Structural and Multidisci-plinary Optimization, Vol. 39, No. 2, pp. 203–215, August 2009.

1228. Maria Jose Gacto, Rafael Alcala and Francisco Herrera, “Adaptation and application of multi-objective evolutionaryalgorithms for rule reduction and parameter tuning of fuzzy rule-based systems”, Soft Computing, Vol. 13, No. 5, pp.419–436, March 2009.

1229. R. Alcala, M.J. Gacto, F. Herrera and J. Alcala-Fdez, “A multi-objective genetic algorithm for tuning and rule selectionto obtain accurate and compact linguistic fuzzy rule-based systems”, International Journal of Uncertainty Fuzziness andKnowledge-Based Systems, Vol. 15, No. 5, pp. 539–557, October 2007.

1230. Dimo Brockhoff, Tobias Friedrich, Nils Hebbinghaus, Christian Klein, Frank Neumann and Eckart Zitzler, “On theEffects of Adding Objectives to Plateau Functions”, IEEE Transactions on Evolutionary Computation, Vol. 13, No. 3,pp. 591–603, July 2009.

1231. Shashi Mittal and Kalyanmoy Deb, “Optimal Strategies of the Iterated Prisoner’s Dilemma Problem for Multiple Con-flicting Objectives”, IEEE Transactions on Evolutionary Computation, Vol. 13, No. 3, pp. 554–565, July 2009.

1232. A.G. Lopez-Herrera, E. Herrera-Viedma and F. Herrera, “A Study of the Use of Multi-Objective Evolutionary Algo-rithms to Learn Boolean Queries: A Comparative Study”, Journal of the American Society for Information Science andTechnology, Vol. 60, No. 6, pp. 1192–1207, June 2009.

1233. Yeboon Yun, Min Yoon and Hirotaka Nakayama, “Multi-objective optimization based on meta-modeling by using supportvector regression”, Optimization and Engineering, Vol. 10, No. 2, pp. 167–181, June 2009.

1234. V. Romero-Garcia, J.V. Sanchez-Perez, L.M. Garcia-Raffi, J.M. Herrero, S. Garcia-Nieto and X. Blasco, “Hole distribu-tion in phononic crystals: Design and optimization”, Journal of the Acoustical Society of America, Vol. 125, No. 6, pp.3774–3783, June 2009.

1235. Eduardo Fernandez, Jorge Navarro and Sergio Bernal, “Multicriteria sorting using a valued indifference relation under apreference disaggregation paradigm”, European Journal of Operational Research, Vol. 198, No. 2, pp. 602–609, October16, 2009.

1236. Dimo Brockhoff and Eckart Zitzler, “Objective Reduction in Evolutionary Multiobjective Optimization: Theory andApplications”, Evolutionary Computation, Vol. 17, No. 2, pp. 135–166, Summer 2009.

1237. Annette Chmielewski, Boris Naujoks, Michael Janas and Uwe Clausen, “Optimizing the Door Assignment in LTL-Terminals”, Transportation Science, Vol. 43, No. 2, pp. 198–210, May 2009.

1238. H.C.W. Lau, T.M. Chan, W.T. Tsui, F.T.S. Chan, G.T.S. Ho, K.L. Choy, “A fuzzy guided multi-objective evolutionaryalgorithm model for solving transportation problem”, Expert Systems with Applications, Vol. 36, No. 4, pp. 8255–8268,May 2009.

1239. Carlos Henggeler Antunes, Dulce Fernao Pires, Carlos Barrico, Alvaro Gomes and Antonio Gomes Martins, “A multi-objective evolutionary algorithm for reactive power compensation in distribution networks”, Applied Energy, Vol. 86,Nos. 7–8, pp. 977–984, July-August 2009.

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1240. Dongdong Yang, Licheng Jiao and Maoguo Gong, “Adaptive Multi-Objective Optimization Based on NondominatedSolutions”, Computational Intelligence, Vol. 25, No. 2, pp. 84–108, May 2009.

1241. V. Romero-Garcia, J.V. Sanchez-Perez, L.M. Garcia-Raffi, J.M. Herrero, S. Garcia-Nieto and X. Blasco, “High opti-mization process for increasing the attenuation properties of acoustic metamaterials by means of the creation of defects”,Applied Physics Letters, Vol. 93, No. 22, Article Number: 223502, December 1, 2008.

Capıtulos de Libros

• Antonio Lopez Jaimes and Carlos A. Coello Coello, “Applications of Parallel Platforms and Models in Evo-lutionary Multi-Objective Optimization”, in Andrew Lewis, Sanaz Mostaghim and Marcus Randall (editors),Biologically-Inspired Optimisation Methods, Chapter 2, pp. 23–49, Springer, Berlin, Germany, 2009, ISBN978-3-642-01261-7.

1. Sergio Santander-Jimenez and Miguel A. Vega-Rodriguez, “Parallel Multiobjective Metaheuristics for Inferring Phylo-genies on Multicore Clusters”, IEEE Transactions on Parallel and Distributed Systems, Vol. 26, No. 6, pp. 1678–1692,June 2015.

• Mohsen Davarynejad, Jos Vrancken, Jan van den Berg, and Carlos A. Coello Coello, “A Fitness GranulationApproach for Large-Scale Structural Design Optimization”, in Raymond Chiong, Thomas Weise and Zbig-niew Michalewicz (editors), Variants of Evolutionary Algorithms for Real-World Applications, pp. 245–280,Springer, Berlin, 2011, ISBN 978-3-642-23423-1.

1. Guo Yu, Jinhua Zheng, Ruimin Shen and Miqing Li, “Decomposing the user-preference in multiobjective optimization”,Soft Computing, Vol. 20, No. 10, pp. 4005–4021, October 2016.

2. A. Kaveh and T. Bakhshpoori, “A new metaheuristic for continuous structural optimization: water evaporation opti-mization”, Structural and Multidisciplinary Optimization, Vol. 54, No. 1, pp. 23–43, July 2016.

3. A. Kaveh and T. Bakhshpoori, “Subspace search mechanism and cuckoo search algorithm for size optimization of spacetrusses”, Steel and Composite Structures, Vol. 18, No. 2, pp. 289–303, February 2015.

• Guillermo Leguizamon and Carlos A. Coello Coello, “Multi-Objective Ant Colony Optimization: A Taxonomyand Review of Approaches”, in Satchidanada Dehuri, Susmita Ghosh and Sung Bae Cho (editors), Integrationof Swarm Intelligence and Artificial Neural Network, Chapter 3, pp. 67–94, World Scientific, Singapore, 2011,ISBN 978-981-4280-14-3.

1. Ruby L.V. Moritz, Enrico Reich, Maik Schwarz, Matthias Bernt and Martin Middendorf, “Refined ranking relations forselection of solutions in multi objective metaheuristics”, European Journal of Operational Research, Vol. 243, No. 2, pp.454–464, June 1, 2015.

2. Miqing Li, Shengxiang Yang and Xiaohui Liu, “Diversity Comparison of Pareto Front Approximations in Many-ObjectiveOptimization”, IEEE Transactions on Cybernetics, Vol. 44, No. 12, pp. 2568–2584, December 2014.

• Juan Carlos Fuentes Cabrera and Carlos A. Coello Coello, “Micro-MOPSO: A Multi-Objective ParticleSwarm Optimizer that Uses a Very Small Population Size”, in Nadia Nedjah, Leandro dos Santos Coelho andLuiza de Macedo de Mourelle (editors), Multi-Objective Swarm Intelligent Systems. Theory & Experiences,Chapter 4, pp. 83–104, Springer, Berlin, Germany, 2010, ISBN 978-3-642-05164-7.

1. Javier Arellano-Verdejo, Adolfo Guzman-Arenas, Salvador Godoy-Calderon and Ricardo Barron Fernandez, “EfficientlyFinding the Optimum Number of Clusters in a Dataset with a New Hybrid Cellular Evolutionary Algorithm”, Com-putacion y Sistemas, Vol. 18, No. 2, pp. 313–327, June 2014.

2. Nadia Smairi, Patrick Siarry, and Khaled Ghedira, “A hybrid particle swarm approach based on Tribes and tabu searchfor multi-objective optimization”, Optimization Methods & Software, Vol. 31, No. 1, pp. 204–231, 2016.

3. Weiyang Tong, Souma Chowdhury and Achille Messac, “A multi-objective mixed-discrete particle swarm optimizationwith multi-domain diversity preservation”, Structural and Multidisciplinary Optimization, Vol. 53, No. 3, pp. 471–488,March 2016.

4. T. Krausse, J. Cullmann, P. Saile and G.H. Schmitz, “Robust multi-objective calibration strategies - possibilities forimproving flood forecasting”, Hydrology and Earth System Sciences, Vol. 16, No. 10, pp. 3579–3606, 2012.

• Alfredo Arias Montano, Carlos A. Coello Coello and Efren Mezura-Montes, “Evolutionary Algorithms Ap-plied to Multi-Objective Aerodynamic Shape Optimization”, in Slawomir Koziel and Xin-She Yang (editors),Computational Optimization, Methods and Algorithms, Chapter 10, pp. 211–240, Springer, Berlin, Germany,2011, ISBN 978-3-642-20858-4.

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1. David J. Munk, Gareth A. Vio and Grant P. Steven, “Topology and shape optimization methods using evolutionaryalgorithms: a review”, Structural and Multidisciplinary Optimization, Vol. 52, No. 3, pp. 613–631, September 2015.

2. Amir Nejat, Pooya Mirzabeygi and Masoud Shariat Panahi, “Airfoil shape optimization using improved MultiobjectiveTerritorial Particle Swarm algorithm with the objective of improving stall characteristics”, Structural and Multidisci-plinary Optimization, Vol. 49, No. 6, pp. 953–967, June 2014.

3. Ni Li, Zeya Su, Zhuming Bi, Chao Tian, Zhiming Ren and Guanghong Gong, “A supportive architecture for CFD-baseddesign optimisation”, Enterprise Information Systems, Vol. 8, No. 2, pp. 246–278, March 4, 2014.

• Carlos A. Coello Coello, “An Introduction to Multi-Objective Particle Swarm Optimizers”, in AntonioGaspar-Cunha, Ricardo Takahashi, Gerald Schaefer and Lino Costa (editors), Soft Computing in Indus-trial Applications, pp. 3–12, Springer, Advances in Intelligent and Soft Computing Series, Vol. 96, Berlin,2011, ISBN 978-3-642-20504-0.

1. Gilberto Reynoso-Meza, Javier Sanchis, Xavier Blasco and Miguel Martinez, “Evolutionary Algorithms for PID controllertuning: Current Trends and Perspectives”, Revista Iberoamericana de Automatica e Informatica Industrial, Vol. 10, No.3, pp. 251–268, July-September 2013.

2. J. Velasco-Carrau, S. Garcia-Nieto, J.V. Salcedo and R.H. Bishop, “Multi-Objective Optimization for Wind Estimationand Aircraft Model Identification”, Journal of Guidance Control and Dynamics, Vol. 39, No. 2, pp. 372–389, February2016.

3. Gilbert Reynoso-Meza, Xavier Blasco, Javier Sanchis and Miguel Martinez, “Controller tuning using evolutionary multi-objective optimisation: Current trends and applications”, Control Engineering Practice, Vol. 28, pp. 58–73, July 2014.

4. J.M. Herrero, G. Reynoso-Meza, M. Martinez, X. Blasco and J. Sanchis, “A Smart-Distributed Pareto Front Using theev-MO GA Evolutionary Algorithm”, International Journal on Artificial Intelligence Tools, Vol. 23, No. 2, ArticleNumber: 1450002, April 2014.

• Luis V. Santana-Quintero, Alfredo Arias Montano and Carlos A. Coello Coello, “A Review of Techniquesfor Handling Expensive Functions in Evolutionary Multi-Objective Optimization”, in Yoel Tenne and Chi-Keong Goh (editors), Computational Intelligence in Expensive Optimization Problems, Chapter 2, pp. 29–59,Springer, Berlin, Germany, 2010, ISBN 978-3-642-10700-9.

1. Gilberto Reynoso-Meza, Javier Sanchis, Xavier Blasco and Miguel Martinez, “Evolutionary Algorithms for PID controllertuning: Current Trends and Perspectives”, Revista Iberoamericana de Automatica e Informatica Industrial, Vol. 10, No.3, pp. 251–268, July-September 2013.

2. Ingrida Steponalice, Sauli Ruuska and Kaisa Miettinen, “A solution process for simulation-based multiobjective designoptimization with an application in the paper industry”, Computer-Aided Design, Vol. 47, pp. 45–58, February 2014.

3. J. Yazdi and S.A.A. Salehi Neyshabouri, “Adaptive surrogate modeling for optimization of flood control detention dams”,Environmental Modelling & Software, Vol. 61, pp. 106–120, November 2014.

4. H.R. Maier, Z. Kapelan, J. Kasprzyk, J. Kollat, L.S. Matott, M.C. Cunha, G.C. Dandy, M.S. Gibbs, E. Keedwell, A.Marchi, A. Ostfeld, D. Savic, D.P. Solomatine, J.A. Vrugt, A.C. Zecchin, B.S. Minsker, E.J. Barbour, G. Kuczera, F.Pasha, A. Castelletti, M. Giuliani and P.M. Reed, “Evolutionary algorithms and other metaheuristics in water resources:Current status, research challenges and future directions”, Environmental Modelling & Software, Vol. 62, pp. 271–299,December 2014.

5. M. Binois, D. Ginsbourger and O. Roustant, “Quantifying uncertainty on Pareto fronts with Gaussian process conditionalsimulations”, European Journal of Operational Research, Vol. 243, No. 2, pp. 386–394, June 1, 2015.

6. Ioannis Tsoukalas and Christos Makropoulos, “Multiobjective optimisation on a budget: Exploring surrogate modellingfor robust multi-reservoir rules generation under hydrological uncertainty”, Environmental Modelling & Software, Vol.69, pp. 396–413, July 2015.

7. Zahra Pourbahman and Ali Hamzeh, “A fuzzy based approach for fitness approximation in multi-objective evolutionaryalgorithms”, Journal of Intelligent & Fuzzy Systems, Vol. 29, No. 5, pp. 2111–2131, 2015.

8. Sanghamitra Bandyopadhyay and Arpan Mukherjee, “An Algorithm for Many-Objective Optimization with ReducedObjective Computations: A Study in Differential Evolution”, IEEE Transactions on Evolutionary Computation, Vol.19, No. 3, pp. 400–413, June 2015.

9. Luc Wismans, Eric Van Berkum and Michiel Bliemer, “Acceleration of Solving the Dynamic Multi-Objective NetworkDesign Problem Using Response Surface Methods”, Journal of Intelligent Transporation Systems, Vol. 18, No. 1, pp.17–29, January 2, 2014.

10. Gilbert Reynoso-Meza, Xavier Blasco, Javier Sanchis and Miguel Martinez, “Controller tuning using evolutionary multi-objective optimisation: Current trends and applications”, Control Engineering Practice, Vol. 28, pp. 58–73, July 2014.

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11. Alexandru-Ciprian Zavoianu, Gerd Bramerdorfer, Edwin Lughofer, Siegfried Silber, Wolfgang Amrhein and Erich PeterKlement, “Hybridization of multi-objective evolutionary algorithms and artificial neural networks for optimizing theperformance of electrical drives”, Engineering Applications of Artificial Intelligence, Vol. 26, No. 8, pp. 1781–1794,September 2013.

12. Jiandao Zhu, Yi-Jen Wang and Matthew Collette, “A multi-objective variable-fidelity optimization method for geneticalgorithms”, Engineering Optimization, Vol. 46, No. 4, pp. 521–542, April 3, 2014.

13. Rommel G. Regis, “Constrained optimization by radial basis function interpolation for high-dimensional expensive black-box problems with infeasible initial points”, Engineering Optimization, Vol. 46, No. 2, pp. 218–243, February 1, 2014.

14. Tsung-Che Chiang, “Enhancing rule-based scheduling in wafer fabrication facilities by evolutionary algorithms: Reviewand opportunity”, Computers & Industrial Engineering, Vol. 64, No. 1, pp. 524–535, January 2013.

15. Minh Nghia Le, Yew Soon Ong, Stefan Menzel, Yaochu Jin and Bernhard Sendhoff, “Evolution by Adapting Surrogates”,Evolutionary Computation, Vol. 21, No. 2, pp. 313–340, Summer 2013.

• Carlos A. Coello Coello, Clarisse Dhaenens and Laetitia Jourdan, “Multi-Objective Combinatorial Opti-mization: Problematic and Context”, in Carlos A. Coello Coello, Clarisse Dhaenens and Laetitia Jourdan(editors), Advances in Multi-Objective Nature Inspired Computing, pp. 1–21, Springer, Berlin, Studies inComputational Intelligence Vol. 272, 2010, ISBN 978-3-642-11217-1.

1. Sergio Santander-Jimenez and Miguel A. Vega-Rodriguez, “Performance evaluation of dominance-based and indicator-based multiobjective approaches for phylogenetic inference”, Information Sciences, Vol. 330, pp. 293–314, February 10,2016.

2. Sergio Santander-Jimenez and Miguel A. Vega-Rodriguez, “Parallel Multiobjective Metaheuristics for Inferring Phylo-genies on Multicore Clusters”, IEEE Transactions on Parallel and Distributed Systems, Vol. 26, No. 6, pp. 1678–1692,June 2015.

3. Sergio Santander-Jimenez and Miguel A. Vega-Rodriguez, “A hybrid approach to parallelize a fast non-dominated sortinggenetic algorithm for phylogenetic inference”, Concurrency and Computation–Practice & Experience, Vol. 27, No. 3,pp. 702–734, March 10, 2015.

4. I-Tung Yang, Yo-Ming Hsieh and Li-Ou Kung, “Parallel Computing Platform for Multiobjective Simulation Optimizationof Bridge Maintenance Planning”, Journal of Construction Engineering and Management–ASCE, Vol. 138, No. 2, pp.215–226, February 2012.

• Efren Mezura-Montes, Lucıa Munoz-Davila and Carlos A. Coello Coello, “A Preliminary Study of Fitness In-heritance in Evolutionary Constrained Optimization”, in Natalio Krasnogor, Giuseppe Nicosia, Mario Pavoneand David Pelta (editors), Nature Inspired Cooperative Strategies for Optimization, pp. 1–14, Springer,Berlin, Germany, 2008, ISBN 978-3-540-78986-4.

1. Ali Kaveh, Karim Laknejadi and Babak Alinejad, “Performance-based multi-objective optimization of large steel struc-tures”, Acta Mechanica, Vol. 223, No. 2, pp. 355–369, February 2012.

• Julio Barrera and Carlos A. Coello Coello, “A Review of Particle Swarm Optimization Methods used for Mul-timodal Optimization”, in Chee-Peng Lim, Lakhmi C. Jain and Satchidananda Dehuri (editors), Innovationsin Swarm Intelligence, Chapter 2, pp. 9–37, Springer-Verlag, Berlin, Germany, 2009, ISBN 978-3-642-04225-6.

1. Joshua T. Knight, David J. Singer and Matthew D. Collette, “Testing of a spreading mechanism to promote diversity inmulti-objective particle swarm optimization”, Optimization and Engineering, Vol. 16, No. 2, pp. 279–302, June 2015.

2. Subhrajit Roy, Sk Minhazul Islam, Swagatam Das and Saurav Ghosh, “Multimodal optimization by artificial weedcolonies enhanced with localized group search optimizers”, Applied Soft Computing, Vol. 13, No. 1, pp. 27–46, January2013.

3. Joshua T. Knight, Frank T. Zahradka, David J. Singer and Matthew D. Collette, “Multiobjective Particle SwarmOptimization of a Planing Craft with Uncertainty”, Journal of Ship Production and Design, Vol. 30, No. 4, pp.194–200, November 2014.

4. Kalyanmoy Deb and Nikhil Padhye, “Enhancing performance of particle swarm optimization through an algorithmiclink with genetic algorithms”, Computational Optimization and Applications, Vol. 57, No. 3, pp. 761–794, April 2014.

5. Aniruddha Basak, Swagatam Das and Kay Chen Tan, “Multimodal Optimization Using a Biobjective Differential Evo-lution Algorithm Enhanced With Mean Distance-Based Selection”, IEEE Transactions on Evolutionary Computation,Vol. 17, No. 5, pp. 666–685, October 2013.

6. Kalyanmoy Deb and Amit Saha, “Multimodal Optimization Using a Bi-Objective Evolutionary Algorithm”, EvolutionaryComputation, Vol. 20, No. 1, pp. 27–62, Spring 2012.

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• Ruhul Sarker and Carlos A. Coello Coello, “Assessment Methodologies for Multiobjective EvolutionaryAlgorithms”, in Ruhul Sarker, Masoud Mohammadian and Xin Yao (Editores), Evolutionary Optimization,Chapter 7, pp. 177–195, Kluwer Academic Publishers, Boston, USA, February 2002, ISBN 0-7923-7654-4.

1. Zbigniew Sekulski, “Multi-objective optimization of high speed vehicle-passenger catamaran by genetic algorithm PartIII Analysis of the results”, Polish Maritime Research, Vol. 18, No. 4, pp. 3–13, 2011.

2. Zbigniew Sekulski, “Multi-objective optimization of high speed vehicle-passenger catamaran by genetic algorithm PartII Computational simulations”, Polish Maritime Research, Vol. 18, No. 3, pp. 3–30, 2011.

• El-Ghazali Talbi, Sanaz Mostaghim, Tatsuya Okabe, Hisao Ishibuchi, Gunter Rudolph and Carlos A. CoelloCoello, “Parallel Approaches for Multi-objective Optimization”, in Jurgen Branke, Kalyanmoy Deb, KaisaMiettinen and Roman Slowinski (editors), Multiobjective Optimization. Interactive and Evolutionary Ap-proaches, pp. 349–372, Springer, Lecture Notes in Computer Science Vol. 5252, Berlin, Germany, 2008.

1. Steffen Limmer and Dietmar Fey, “Porting of the transfer-matrix method for multilayer thin-film computations ongraphics processing units”, Optical Engineering, Vol. 52, No. 7, Article Number: 075103, July 2013.

2. Danilo Vasconcellos Vargas, Junichi Murata, Hirotaka Takano and Alexandre Claudio Botazzo Delbem, “General Sub-population Framework and Taming the Conflict Inside Populations”, Evolutionary Computation, Vol. 23, No. 1, pp.1–36, 2015.

3. Hossein Rajabalipour Cheshmehgaz, Mohammad Ishak Desa and Antoni Wibowo, “Effective local evolutionary searchesdistributed on an island model solving bi-objective optimization problems”, Applied Intelligence, Vol. 38, No. 3, pp.331–356, April 2013.

4. Hossein Rajabalipour Cheshmehgaz, Habibollah Haron and Abdollah Sharifi, “The review of multiple evolutionarysearches and multi-objective evolutionary algorithms”, Artificial Intelligence Review, Vol. 43, No. 3, pp. 311–343,March 2015.

5. Bilel Derbel, Jeremie Humeauc, Arnaud Liefooghe and Sebastien Verel, “Distributed localized bi-objective search”,European Journal of Operational Research, Vol. 239, No. 3, pp. 731–743, December 16, 2014.

6. Hossein Rajabalipour Cheshmehgaz, Mohamad Ishak Desa and Antoni Wibowo, “An effective model of multiple multi-objective evolutionary algorithms with the assistance of regional multi-objective evolutionary algorithms: VIPMOEAs”,Applied Soft Computing, Vol. 13, No. 5, pp. 2863–2895, May 2013.

7. Matjaz Depolli, Roman Trobec and Bogdan Filipic, “Asynchronous Master-Slave Parallelization of Differential Evolutionfor Multi-Objective Optimization”, Evolutionary Computation, Vol. 21, No. 2, pp. 261–291, Summer 2013.

8. Van Vinh Nguyen, Dietrich Hartmann and Markus Konig, “A distributed agent-based approach for simulation-basedoptimization”, Advanced Engineering Informatics, Vol. 26, No. 4, pp. 814–832, October 2012.

9. Christian Grimme, Joachim Lepping and Alexander Papaspyrou, “Parallel predator-prey interaction for evolutionarymulti-objective optimization”, Natural Computing, Vol. 11, No. 3, pp. 519–533, September 2012.

10. Nima Safaei, Dragan Banjevic and Andrew K.S. Jardine, “Multi-threaded simulated annealing for a bi-objective main-tenance scheduling problem”, International Journal of Production Research, Vol. 50, No. 1, pp. 63–80, 2012.

11. Gualtiero Colombo and Stuart M. Allen, “A comparison of problem decomposition techniques for the FAP”, Journal ofHeuristics, Vol. 16, No. 3, pp. 259–288, June 2010.

12. Tomas Petkus, Ernestas Filatovas and Olga Kurasova, “Investigation of Human Factors while Solving Multiple CriteriaOptimization Problems in Computer Network”, Technological and Economic Development of Economy, Vol. 15, No. 3,pp. 464–479, 2009.

• Antonio Lopez Jaimes, Luis Vicente Santana Quintero and Carlos A. Coello Coello, “Ranking Methodsin Many-objective Evolutionary Algorithms”, in Raymond Chiong (editor), Nature-Inspired Algorithms forOptimisation, Chapter 15, pp. 413–434, Springer, Berlin, Germany, 2009, ISBN 978-3-642-00266-3.

1. Miqing Li, Shengxiang Yang and Xiaohui Liu, “Pareto or Non-Pareto: Bi-Criterion Evolution in Multiobjective Opti-mization”, IEEE Transactions on Evolutionary Computation, Vol. 20, No. 5, pp. 645–665, October 2016.

2. Miqing Li, Shengxiang Yang and Xiaohui Liu, “Bi-goal evolution for many-objective optimization problems”, ArtificialIntelligence, Vol. 228, pp. 45–65, November 2015.

3. Miqing Li, Shengxiang Yang and Xiaohui Liu, “Diversity Comparison of Pareto Front Approximations in Many-ObjectiveOptimization”, IEEE Transactions on Cybernetics, Vol. 44, No. 12, pp. 2568–2584, December 2014.

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34. R. Alcala, M.J. Gacto, F. Herrera and J. Alcala-Fdez, “A multi-objective genetic algorithm for tuning and rule selectionto obtain accurate and compact linguistic fuzzy rule-based systems”, International Journal of Uncertainty Fuzziness andKnowledge-Based Systems, Vol. 15, No. 5, pp. 539–557, October 2007.

35. Dimo Brockhoff and Eckart Zitzler, “Objective Reduction in Evolutionary Multiobjective Optimization: Theory andApplications”, Evolutionary Computation, Vol. 17, No. 2, pp. 135–166, Summer 2009.

36. Dongdong Yang, Licheng Jiao and Maoguo Gong, “Adaptive Multi-Objective Optimization Based on NondominatedSolutions”, Computational Intelligence, Vol. 25, No. 2, pp. 84–108, May 2009.

• Carlos A. Coello Coello, “Evolutionary Multiobjective Optimization: Current and Future Challenges”, inJose Benitez, Oscar Cordon, Frank Hoffmann and Rajkumar Roy (editors), Advances in Soft Computing—Engineering, Design and Manufacturing, pp. 243–256, Springer-Verlag, ISBN 1-85233-755-9, September2003.

1. Fabiany Lamboia, Lucia Valeria Ramos de Arruda and Flavio Neves, Jr., “Modified Shuffled Frog Leaping Algorithmfor Improved Pareto-Set Computation: Application to Product Transport in Pipeline Networks”, Journal of ControlAutomation and Electrical Systems, Vol. 27, No. 1, pp. 43–59, February 2016.

2. Paulo Cesar Ribas, Lia Yamamoto, Helton Luis Polli, L.V.R. Arruda and Flavio Neves, Jr., “A micro-genetic algorithmfor multi-objective scheduling of a real world pipeline network”, Engineering Applications of Artificial Intelligence, Vol.26, No. 1, pp. 302–313, January 2013.

3. Peter von Buelow, “Suitability of genetic based exploration in the creative design process”, Digital Creativity, Vol. 19,No. 1, pp. 51–61, 2008.

4. Olcay Ersel Canyurt and Prabhat Hajela, “Cellular genetic algorithm technique for the multicriterion design optimiza-tion”, Structural and Multidisciplinary Optimization, Vol. 40, Nos. 1–6, pp. 201–214, January 2010.

5. Maoguo Gong, Licheng Jiao, Haifeng Du and Liefeng Bo, “Multiobjective immune algorithm with nondominatedneighbor-based selection”, Evolutionary Computation, Vol. 16, No. 2, pp. 225–255, Summer 2008.

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6. Antonio Pinto, Daniele Peri and Emilio F. Campana, “Multiobjective optimization of a containership using deterministicparticle swarm optimization”, Journal of Ship Research, Vol. 51, No. 3, pp. 217–228, September 2007.

7. Wangshu Yao, Chen Shifu and Chen Zhaoqian, “SDMOGA: A New Multi-objective Genetic Algorithm Based on Ob-jective Space Divided”, in Irwin King, Jun Wang, Laiwan Chan and DeLiang L. Wang (editors), Neural InformationProcessing, 13th International Conference, ICONIP 2006, Part III, pp. 754–762, Springer-Verlag. Lecture Notes inComputer Science Vol. 4234, Hong Kong, China, October 2006.

8. L. Grandinetti, F. Guerriero, G. Lepera and M. Mancini, “A niched genetic algorithm to solve a pollutant emissionreduction problem in the manufacturing industry: A case study”, Computers & Operations Research, Vol. 34, No. 7,pp. 2191–2214, July 2007.

9. MaoGuo Gong, LiCheng Jiao, WenPing Ma and HaiFeng Du, “Multiobjective optimization using an immunodominanceand clonal selection inspired algorithm”, Science in China Series F–Information Sciences, Vol. 51, No. 8, pp. 1064–1082,August 2008.

• Dragan Cvetkovic and Carlos A. Coello Coello, “Human Preferences and Their Applications in EvolutionaryMulti-Objective Optimization”, in Yaochu Jin (editor) Knowledge Incorporation in Evolutionary Compu-tation, Springer, pp. 479–502, Studies in Fuzziness and Soft Computing Vol. 167, ISBN 3-540-22902-7,2005.

1. D. Greiner, J.M. Emperador, B. Galvan, M. Mendez and G. Winter, “Engineering Knowledge-Based Variance-ReductionSimulation and G-Dominance for Structural Frame Robust Optimization”, Advances in Mechanical Engineering, ArticleNumber: 680359, 2013.

2. David Coulot, Arnaud Pollet, Xavier Collilieux and Philippe Berio, “Global optimization of core station networks forspace geodesy: application to the referencing of the SLR EOP with respect to ITRF”, Journal of Geodesy, Vol. 84, No.1, pp. 31–50, January 2010.

• Efren Mezura-Montes and Carlos A. Coello Coello, “Constrained Optimization via Multiobjective Evolution-ary Algorithms”, in Joshua Knowles, David Corne and Kalyanmoy Deb (Editors), Multi-Objective ProblemSolving from Nature: From Concepts to Applications, pp. 53–75, Springer, 2008, ISBN 978-3-540-72963-1.

1. Eman Sayed, Daryl Essam, Ruhul Sarker and Saber Elsayed, “Decomposition-based evolutionary algorithm for largescale constrained problems”, Information Sciences, Vol. 316, pp. 457–486, September 20, 2015.

2. Minami Miyakawa, Keiki Takadama and Hiroyuki Sato, “Controlling selection areas of useful infeasible solutions fordirected mating in evolutionary constrained multi-objective optimization”, Annals of Mathematics and Artificial Intel-ligence, Vol. 76, Nos. 1-2, pp. 25–46, February 2016.

3. Rituparna Datta and Kalyanmoy Deb, “Uniform adaptive scaling of equality and inequality constraints within hybridevolutionary-cum-classical optimization”, Soft Computing, Vol. 20, No. 6, pp. 2367–2382, June 2016.

4. Andrea Maesani, Giovanni Iacca and Dario Floreano, “Memetic Viability Evolution for Constrained Optimization”,IEEE Transactions on Evolutionary Computation, Vol. 20, No. 1, pp. 125–144, February 2016.

5. Amir H. Gandomi, “Interior search algorithm (ISA): A novel approach for global optimization”, ISA Transactions, Vol.53, No. 4, pp. 1168–1183, July 2014.

6. A. Villagra, D. Pandolfi and G. Leguizamon, “ Handling constraints with an evolutionary tool for scheduling oil wellsmaintenance visits”, Engineering Optimization, Vol. 45, No. 8, pp. 963–981, July-September, 2013.

7. Blaze Gjorgiev and Marko Cepin, “A multi-objective optimization based solution for the combined economic-environmentalpower dispatch problem”, Engineering Applications of Artificial Intelligence, Vol. 26, No. 1, pp. 417–429, January 2013.

8. Romanas Puisa and Heinrich Streckwall, “Prudent constraint-handling technique for multiobjective propeller optimisa-tion”, Optimization and Engineering, Vol. 12, No. 4, pp. 657–680, December 2011.

9. Andreas Konstantinidis and Kun Yang, “Multi-objective K-connected Deployment and Power Assignment in WSNsusing a problem-specific constrained evolutionary algorithm based on decomposition”, Computer Communications, Vol.34, No. 1, pp. 83–98, January 15, 2011.

10. Yong Wang, Zixing Cai and Yuren Zhou, “Accelerating adaptive trade-off model using shrinking space technique forconstrained evolutionary optimization”, International Journal for Numerical Methods in Engineering, Vol. 77, No. 11,pp. 1501–1534, March 2009.

11. Dimo Brockhoff, Tobias Friedrich, Nils Hebbinghaus, Christian Klein, Frank Neumann and Eckart Zitzler, “On theEffects of Adding Objectives to Plateau Functions”, IEEE Transactions on Evolutionary Computation, Vol. 13, No. 3,pp. 591–603, July 2009.

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Journals Internacionales

• Qiuzhen Lin, Zhiwang Liu, Qiao Yan, Zhihua Du, Carlos A. Coello Coello, Zhengping Liang, Wenjun Wangand Jianyong Chen, “Adaptive composite operator selection and parameter control for multiobjective evo-lutionary algorithm”, Information Sciences, Vol. 339, pp. 332–352, April 20, 2016.

1. Zi-Ke Zhang, Chuang Liu, Xiu-Xiu Zhan, Xin Lu, Chu-Xu Zhang and Yi-Cheng Zhang, “Dynamics of informationdiffusion and its applications on complex networks”, Physics Reports–Review Section of Physics Letters, Vol. 651, pp.1–34, September 26, 2016.

2. Sandra M. Venske, Richard A. Goncalves, Elaine M. Benelli and Myriam R. Delgado, “ADEMO/D: An adaptive dif-ferential evolution for protein structure prediction problem”, Expert Systems with Applications, Vol. 56, pp. 209–226,September 1, 2016.

• Qiuzhen Lin, Jianyong Chen, Zhi-Hui Zhan, Wei-neng Chen, Carlos A. Coello Coello, Yilong Yin, Chih-MinLin and Jun Zhang, “A Hybrid Evolutionary Immune Algorithm for Multiobjective Optimization Problems”,IEEE Transactions on Evolutionary Computation, Vol. 20, No. 5, pp. 711–728, October 2016.

1. Xiuhong Cai, Xiang Li, Hong Qi, Fang Wei, Jianyong Chen and Jianwei Shuai, “Comparison of gating dynamics ofdifferent IP3R channels with immune algorithm searching for channel parameter distributions”, Physical Biology, Vol.13, No. 5, Article Number: 056005, October 2016.

2. Miguel A. Medina, Juan M. Ramırez, Carlos A. Coello and Swagatam Das, “Use of a multi-objectiveteaching-learning algorithm for reduction of power losses in a power test system”, DYNA, Vol. 81, No.185, pp. 196–203, June 2014, ISSN 0012-7353.

1. Jose Antonio Marmolejo-Saucedo and Roman Rodrıguez-Aguilar, “Short-term generation planning by primal and dualdecomposition techniques”, DYNA, Vol. 82, No. 191, pp. 58–62, 2015.

• Gustavo Zavala, Antonio J. Nebro, Francisco Luna and Carlos A. Coello Coello, “A Survey of Multi-ObjectiveMetaheuristics Applied to Structural Optimization”, Structural and Multidisciplinary Optimization, Vol. 49,No. 4, pp. 537–558, April 2014.

1. Yuki Sato, Kazuhiro Izui, Takayuki Yamada and Shinji Nishiwaki, “Gradient-based multiobjective optimization using adistance constraint technique and point replacement”, Engineering Optimization, Vol. 48, No. 7, pp. 1226–1250, 2016.

2. S. Salcedo-Sanz, A. Pastor-Sanchez, J.A. Portilla-Figueras and L. Prieto, “Effective multi-objective optimization withthe coral reefs optimization algorithm”, Engineering Optimization, Vol. 48, No. 6, pp. 966–984, June 2, 2016.

3. Fred Edmond Boafo, Jin-Hee Kim and Jun-Tae Kim, “Performance of Modular Prefabricated Architecture: Case Study-Based Review and Future Pathways”, Sustainability, Vol. 8, No. 6, Article Number: 558, June 2016.

4. D.E.C. Vargas, A.C.C. Lemonge, H.J.C. Barbosa and H.S. Bernardino, “A differential evolution based algorithm forconstrained multiobjective structural optimization problems”, Revista Internacional de Metodos Numericos para Calculoy Diseno en Ingenierıa, Vol. 32, No. 2, pp. 91–99, April-June 2016.

5. Anupam Yadav and Kusum Deep, “A shrinking hypersphere PSO for engineering optimisation problems”, Journal ofExperimental & Theoretical Artificial Intelligence, Vol. 28, Nos. 1-2, pp. 1–33, March 3, 2016.

6. Hugo Rocha, Igor S. Peretta, Gerson Flavio M. Lima, Leonardo G. Marques and Keiji Yamanaka, “Exterior lightingcomputer-automated design based on multi-criteria parallel evolutionary algorithm: optimized designs for illuminationquality and energy efficiency”, Expert Systems with Applications, Vol. 45, pp. 208–222, March 1, 2016.

7. Yuki Sato, Kazuhiro Izui, Takayuki Yamada and Shinji Nishiwaki, “Gradient-based multiobjective optimization using adistance constraint technique and point replacement”, Engineering Optimization, Vol. 48, No. 7, pp. 1226–1250, 2016.

8. G.R. Meza, H.S. Sanchez, L.d.S. Coelho and R. Zanetti, “Multidisciplinary Optimisation in Mechatronic Systems: AComparative Analysis with Multiobjective Techniques”, IEEE Latin America Transactions, Vol. 14, No. 1, pp. 364–370,January 2016.

9. Edward M. Segal, Landolf Rhode-Barbarigos, Sigrid Adriaenssens and Rajan D. Filomeno Coelho, “Multi-objectiveoptimization of polyester-rope and steel-rope suspended footbridgese”, Engineering Structures, Vol. 99, pp. 559–567,September 15, 2015.

• Carlos Segura, Carlos A. Coello Coello, Eduardo Segredo and Coromoto Leon, “On the Adaptation of theMutation Scale Factor in Differential Evolution”, Optimization Letters, pp. 189–198, Vol. 9, No. 1, January2015.

1. Elena-Niculina Dragoi and Vlad Dafinescu, “Parameter control and hybridization techniques in differential evolution: asurvey”, Artificial Intelligence Review, Vol. 45, No. 4, pp. 447–470, April 2016.

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• Carlos Segura, Carlos A. Coello Coello and Alfredo G. Hernandez-Dıaz, “Improving the Vector GenerationStrategy of Differential Evolution for Large-Scale Optimization”, Information Sciences, Vol. 323, pp. 106–129, 1 December 2015.

1. P. Lopez-Garcia, E. Onieva, E. Osaba, A.D. Masegosa and A. Perallos, “GACE: A meta-heuristic based in the hybridiza-tion of Genetic Algorithms and Cross Entropy methods for continuous optimization”, Expert Systems with Applications,Vol. 55, pp. 508–519, August 15, 2016.

2. Anupam Trivedi, Dipti Srinivasan, Subhodip Biswas and Thomas Reindl, “A genetic algorithm - differential evolutionbased hybrid framework: Case study on unit commitment scheduling problem”, Information Sciences, Vol. 354, pp.275–300, August 1, 2016.

3. Guowei Wang, Chunhua Yang, Hongqiu Zhu, Yonggang Li, Xiongwei Peng and Weihua Gui, “State-transition-algorithm-based resolution for overlapping linear sweep voltammetric peaks with high signal ratio”, Chemometrics and IntelligentLaboratory Systems, Vol. 151, pp. 61–70, February 15, 2016.

• Miguel A. Medina, Carlos A. Coello Coello and Juan M. Ramirez, “Reactive Power Handling by a Multi-Objective Teaching Learning Optimizer based on Decomposition”, IEEE Transactions on Power Systems,Vol. 28, No. 4, pp. 3629–3637, November 2013.

1. Kunjie Yu, Xin Wang and Zhenlei Wang, “Constrained optimization based on improved teaching-learning-based opti-mization algorithm”, Information Sciences, Vol. 352, pp. 61–78, July 20, 2016.

• Oliver Schuetze, Adriana Lara and Carlos A. Coello Coello, “Evolutionary Continuation Methods for Opti-mization Problems”, in 2009 Genetic and Evolutionary Computation Conference (GECCO’2009), pp. 651–658, ACM Press, Montreal, Canada, July 8–12, 2009, ISBN 978-1-60558-325-9.

1. Benjamin Martin, Alexandre Goldsztejn, Laurent Granvilliers and Christophe Jermann, “On continuation methods fornon-linear bi-objective optimization: towards a certified interval-based approach”, Journal of Global Optimization, Vol.64, pp. 3–16, January 2016.

• Salvador Botello, Arturo Hernandez, Giovanni Lizarraga y Carlos Coello, “ISPAES: a new evolutionaryalgorithm for the optimization of one or many objective functions with constraints”, Revista Internacionalde Metodos Numericos para Calculo y Diseno en Ingenierıa, Vol. 20, No. 2, pp. 139–167, 2004.

1. A. Ramirez, I. Lopez, Y. Villuendas and C. Yanez, “Evolutive Improvement of Parameters in an Associative Classifier”,IEEE Latin America Transactions, Vol. 13, No. 5, pp. 1550–1555, May 2015.

• Edgar A. Portilla-Flores, Efren Mezura-Montes, Jaime Alvarez-Gallegos, Carlos A. Coello-Coello and CarlosA. Cruz-Villar, “Integration of Structure and Control Using an Evolutionary Approach: An Application tothe Optimal Concurrent Design of a CVT”, International Journal for Numerical Methods in Engineering,Vol. 71, No. 8, pp. 883–901, August 2007.

1. Ping Liu, Guodong Li and Xinggao Liu, “Fast engineering optimization: A novel highly effective control parameterizationapproach for industrial dynamic processes”, ISA Transactions, Vol. 58, pp. 248–254, September 2015.

• Alejandro Rosales-Perez, Jesus A. Gonzalez, Carlos A. Coello Coello, Hugo Jair Escalante and Carlos A.Reyes-Garcia, “Surrogate-Assisted Multi-Objective Model Selection for Support Vector Machines”, Neuro-computing, Vol. 150, Part A, pp. 163–172, February 2015.

1. Lucas M. Pavelski, Myriam R. Delgado, Carolina P. Almeida, Richard A. Goncalves and Sandra M. Venske, “ExtremeLearning Surrogate Models in Multi-objective Optimization based on Decomposition”, Neurocomputing, Vol. 180, pp.55–67, March 5, 2016.

• Anirban Mukhopadhyay, Ujjwal Maulik, Sanghamitra Bandyophadyay and Carlos Artemio Coello Coello,“A Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part I”, IEEE Transactions onEvolutionary Computation, Vol. 18, No. 1, pp. 4–19, February 2014.

1. Alberto Cano, Sebastian Ventura and Krzysztof J. Cios, “Multi-objective genetic programming for feature extractionand data visualization”, Soft Computing, Vol. 21, No. 8, pp. 2069–2089, April 2017.

2. Alejandro Cervantes, David Quintana and Gustavo Recio, “Efficient dynamic resampling for dominance-based multiob-jective evolutionary optimization”, Engineering Optimization, Vol. 49, No. 2, pp. 311–327, February 2017.

3. Handing Wang, Yaochu Jin and Jan O. Jansen, “Data-Driven Surrogate-Assisted Multiobjective Evolutionary Optimiza-tion of a Trauma System”, IEEE Transactions on Evolutionary Computation, Vol. 20, No. 6, pp. 939–952, December2016.

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4. Xin Qiu, Jian-Xin Xu, Kay Chen Tan and Hussein A. Abbass, “Adaptive Cross-Generation Differential EvolutionOperators for Multiobjective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 20, No. 2, pp.232–244, April 2016.

5. Yiu-ming Cheung, Fangqing Gu and Hai-Lin Liu, “Objective Extraction for Many-Objective Optimization Problems:Algorithm and Test Problems”, IEEE Transactions on Evolutionary Computation, Vol. 20, No. 5, pp. 755–772, October2016.

6. Alberto Fernandez, Victoria Lopez, Maria Jose del Jesus and Francisco Herrera, “Revisiting Evolutionary Fuzzy Systems:Taxonomy, applications, new trends and challenges”, Knowledge-Based Systems, Vol. 80, pp. 109–121, May 2015.

7. S. Salcedo-Sanz, A. Pastor-Sanchez, J.A. Portilla-Figueras and L. Prieto, “Effective multi-objective optimization withthe coral reefs optimization algorithm”, Engineering Optimization, Vol. 48, No. 6, pp. 966–984, June 2, 2016.

8. Min Han and Weijie Ren, “Global mutual information-based feature selection approach using single-objective and multi-objective optimization”, Neurocomputing, Vol. 168, pp. 47–54, November 30, 2015.

9. Guo-Qiang Zeng, Jie Chen, Li-Min Li, Min-Rong Chen, Lie Wu, Yu-Xing Dai and Chong-Wei Zheng, “An improvedmulti-objective population-based extremal optimization algorithm with polynomial mutation”, Information Sciences,Vol. 330, pp. 49–73, February 10, 2016.

10. Seungseob Lee, SuKyoung Lee, Kyungsoo Kim and Yoon Hyuk Kim, “Base Station Placement Algorithm for Large-ScaleLTE Heterogeneous Networks”, Plos One, Vol. 10, No. 10, Article Number: e0139190, October 13, 2015.

11. Bingdong Li, Jinlong Li, Ke Tang and Xin Yao, “Many-Objective Evolutionary Algorithms: A Survey”, ACM ComputingSurveys, Vol. 48, No. 1, Article Number: 13, September 2015.

• Anirban Mukhopadhyay, Ujjwal Maulik, Sanghamitra Bandyophadyay and Carlos Artemio Coello Coello,“Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part II”, IEEE Transactions on Evo-lutionary Computation, Vol. 18, No. 1, pp. 20–35, February 2014.

1. Xin Qiu, Jian-Xin Xu, Kay Chen Tan and Hussein A. Abbass, “Adaptive Cross-Generation Differential EvolutionOperators for Multiobjective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 20, No. 2, pp.232–244, April 2016.

2. Alejandro Cervantes, David Quintana and Gustavo Recio, “Efficient dynamic resampling for dominance-based multiob-jective evolutionary optimization”, Engineering Optimization, Vol. 49, No. 2, pp. 311–327, February 2017.

3. S. Salcedo-Sanz, A. Pastor-Sanchez, J.A. Portilla-Figueras and L. Prieto, “Effective multi-objective optimization withthe coral reefs optimization algorithm”, Engineering Optimization, Vol. 48, No. 6, pp. 966–984, June 2, 2016.

4. Ailong Ma, Yanfei Zhong and Liangpei Zhang, “Adaptive Multiobjective Memetic Fuzzy Clustering Algorithm for RemoteSensing Imagery”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 53, No. 8, pp. 4202–4217, August 2015.

5. Guo-Qiang Zeng, Jie Chen, Li-Min Li, Min-Rong Chen, Lie Wu, Yu-Xing Dai and Chong-Wei Zheng, “An improvedmulti-objective population-based extremal optimization algorithm with polynomial mutation”, Information Sciences,Vol. 330, pp. 49–73, February 10, 2016.

6. Bingdong Li, Jinlong Li, Ke Tang and Xin Yao, “Many-Objective Evolutionary Algorithms: A Survey”, ACM ComputingSurveys, Vol. 48, No. 1, Article Number: 13, September 2015.

• Antonio Lopez Jaimes, Carlos A. Coello Coello, Hernan Aguirre and Kiyoshi Tanaka, “Objective SpacePartitioning Using Conflict Information for Solving Many-Objective Problems”, Information Sciences, Vol.268, pp. 305–327, 1 June 2014.

1. Yiu-ming Cheung, Fangqing Gu and Hai-Lin Liu, “Objective Extraction for Many-Objective Optimization Problems:Algorithm and Test Problems”, IEEE Transactions on Evolutionary Computation, Vol. 20, No. 5, pp. 755–772, October2016.

2. Handing Wang and Xin Yao, “Objective reduction based on nonlinear correlation information entropy”, Soft Computing,Vol. 20, No. 6, pp. 2393–2407, June 2016.

3. Wiem Mkaouer, Marouane Kessentini, Adnan Shaout, Patrice Koligheu, Slim Bechikh, Kalyanmoy Deb and Ali Ouni,“Many-Objective Software Remodularization Using NSGA-III”, ACM Transactions on Software Engineering and Method-ology, Vol. 24, No. 3, Article Number: 17, May 2015.

• Francisco Luna, Gustavo R. Zavala, Antonio J. Nebro, Juan J. Durillo and Carlos A. Coello Coello, “Dis-tributed multi-objective metaheuristics for real-world structural optimization problems”, The ComputerJournal, Vol. 59, No. 6, pp. 777–792, June 2016.

1. Yuki Sato, Kazuhiro Izui, Takayuki Yamada and Shinji Nishiwaki, “Gradient-based multiobjective optimization using adistance constraint technique and point replacement”, Engineering Optimization, Vol. 48, No. 7, pp. 1226–1250, 2016.

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2. Ke Li, Sam Kwong and Kalyanmoy Deb, “A dual-population paradigm for evolutionary multiobjective optimization”,Information Sciences, Vol. 309, pp. 50–72, July 10, 2015.

• Jorge E. Rodrıguez, Andres L. Medaglia and Carlos A. Coello Coello, “Design of a motorcycle frame usingneuroacceleration strategies in MOEAs”, Journal of Heuristics, Vol. 15, No. 2, pp. 177–196, April 2009.

1. Joseph Y.J. Chow and Amelia C. Regan, “A surrogate-based multiobjective metaheuristic and network degradationsimulation model for robust toll pricing”, Optimization and Engineering, Vol. 15, No. 1, pp. 137–165, March 2014.

• Antonio Lopez Jaimes and Carlos A. Coello Coello, “Including preferences into a multiobjective evolutionaryalgorithm to deal with many-objective engineering optimization problems”, Information Sciences, Vol. 277,pp. 1–20, September 1, 2014.

1. Shahin Rostami and Ferrante Neri, “ Covariance matrix adaptation pareto archived evolution strategy with hypervolume-sorted adaptive grid algorithm”, Integrated Computer-Aided Engineering, Vol. 23, No. 4, pp. 313–329, 2016.

2. Baehyun Min, Changhyup Park, Ilsik Jang, Joe M. Kang and Sunghoon Chung, “Development of Pareto-based evo-lutionary model integrated with dynamic goal programming and successive linear objective reduction”, Applied SoftComputing, Vol. 35, pp. 75–112, October 2015.

3. Ernestas Filatovas, Olga Kurasova and Karthik Sendhya, “Synchronous R-NSGA-II: An Extended Preference-BasedEvolutionary Algorithm for Multi-Objective Optimization”, Informatica, Vol. 26, No. 1, pp. 33–50, 2015.

• Carlos A. Coello Coello, “Research Directions in Evolutionary Multi-Objective Optimization. Current andFuture Research Topics”, Transactions of the Japanese Society for Evolutionary Computation, Vol. 3, No.3, pp. 110–121, December 2012.

1. Shahin Rostami and Ferrante Neri, “ Covariance matrix adaptation pareto archived evolution strategy with hypervolume-sorted adaptive grid algorithm”, Integrated Computer-Aided Engineering, Vol. 23, No. 4, pp. 313–329, 2016.

2. Shahin Rostami, Dean O’Reilly, Alex Shenfield and Nicholas Bowring, “A novel preference articulation operator for theEvolutionary Multi-Objective Optimisation of classifiers in concealed weapons detection”, Information Sciences, Vol.295, pp. 494–520, February 20, 2015.

• Victoria S. Aragon, Susana C. Esquivel and Carlos A. Coello Coello, “A T-Cell Algorithm for Solving DynamicOptimization Problems”, Information Sciences, Vol. 181, No. 17, pp. 3614–3637, 1 September 2011.

1. K. Chandrasekaran, Sishaj P. Simon and Narayana Prasad Padhy, “Binary real coded firefly algorithm for solving unitcommitment problem”, Information Sciences, Vol. 249, pp. 67–84, November 10, 2013.

2. Min Han, Chuang Liu and Jun Xing, “An evolutionary membrane algorithm for global numerical optimization problems”,Information Sciences, Vol. 276, pp. 219–241, August 20, 2014.

3. Motjabe Ghasemi, Mohammad Mehdi Ghanbarian, Sahand Ghavidel, Shima Rahmani and Esmaeil Mahboubi Moghad-dam, “Modified teaching learning algorithm and double differential evolution algorithm for optimal reactive powerdispatch problem: A comparative study”, Information Sciences, Vol. 278, pp. 231–249, September 10, 2014.

4. Changhe Li, Thanh Nguyen Trung, Ming Yang, Shengxiang Yang and Sanyou Zeng, “Multi-population methods inunconstrained continuous dynamic environments: The challenges”, Information Sciences, Vol. 296, pp. 95–118, March1, 2015.

• D.A. Bloch and C.A. Coello Coello, “Smiling at Evolution”, Applied Soft Computing, Vol. 11, No. 8, pp.5724–5734, December 2011.

1. Adam P. Piotrowski, “Differential Evolution algorithms applied to Neural Network training suffer from stagnation”,Applied Soft Computing, Vol. 21, pp. 382–406, August 2014.

• Antonin Ponsich and Carlos A. Coello Coello, “A Hybrid Differential Evolution-Tabu Search Algorithm forthe Solution of Job-Shop Scheduling Problems”, Applied Soft Computing, Vol. 13, No. 1, pp. 462–474,January 2013.

1. Rapeepan Pitakaso and Kanchana Sethanan, “Modified differential evolution algorithm for simple assembly line balancingwith a limit on the number of machine types”, Engineering Optimization, Vol. 48, No. 2, pp. 253–271, February 1, 2016.

2. Mohamed Kurdi, “A new hybrid island model genetic algorithm for job shop scheduling problem”, Computers & Indus-trial Engineering, Vol. 88, pp. 273–283, October 2015.

3. Liang Gao, Xinyu Li, Xiaoyu Wen, Chao Lu and Feng Wen, “A hybrid algorithm based on a new neighborhood structureevaluation method for job shop scheduling problem”, Computers & Industrial Engineering, Vol. 88, pp. 417–429, October2015.

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4. Raul Mencia, Maria R. Sierra, Carlos Mencia and Ramiro Varela, “Memetic algorithms for the job shop schedulingproblem with operators”, Applied Soft Computing, Vol. 34, pp. 94–105, September 2015.

5. Mohammad Rohaninejad, Amir Saman Kheirkhah, Behdin Vahedi Nouri and Parviz Fattahi, “Two hybrid tabu search-firefly algorithms for the capacitated job shop scheduling problem with sequence-dependent setup cost”, InternationalJournal of Computer Integrated Manufacturing, Vol. 28, No. 5, pp. 470–487, May 4, 2015.

6. Mehrdad Amirghasemi and Reza Zamani, “A synergetic combination of small and large neighborhood schemes in devel-oping an effective procedure for solving the job shop scheduling problem”, SpringerPlus, Vol. 3, Article Number: 193,April 16, 2014.

7. Adam P. Piotrowski, “Differential Evolution algorithms applied to Neural Network training suffer from stagnation”,Applied Soft Computing, Vol. 21, pp. 382–406, August 2014.

8. Hao Gao, Sam Kwong, Baojie Fan and Ran Wang, “A Hybrid Particle-Swarm Tabu Search Algorithm for Solving JobShop Scheduling Problems”, IEEE Transactions on Industrial Informatics, Vol. 10, No. 4, pp. 2044–2054, November2014.

• Eduardo Fernandez, Edy Lopez, Gustavo Mazcorro, Rafael Olmedo and Carlos A. Coello Coello, “Applicationof the non-outranked sorting genetic algorithm to public project portfolio selection”, Information Sciences,Vol. 228, pp. 131–149, 10 April 2013.

1. Matthew L. Wallace and Ismael Rafols, “Research Portfolio Analysis in Science Policy: Moving from Financial Returnsto Societal Benefits”, Minerva, Vol. 53, No. 2, pp. 89–115, June 2015.

2. Emma M. Sanchez, Julio B. Clempner and Alexander S. Poznyak, “Solving the mean-variance customer portfolio inMarkov chains using iterated quadratic/Lagrange programming: A credit-card customer limits approach”, Expert Sys-tems with Applications, Vol. 42, No. 12, pp. 5315–5327, July 15, 2015.

3. K. Liagkouras and K. Metaxiotis, “Efficient Portfolio Construction with the Use of Multiobjective Evolutionary Algo-rithms: Best Practices and Performance Metrics”, International Journal of Information Technology & Decision Making,Vol. 14, No. 3, pp. 535–564, May 2015.

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1. Xin Qiu, Jian-Xin Xu, Kay Chen Tan and Hussein A. Abbass, “Adaptive Cross-Generation Differential EvolutionOperators for Multiobjective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 20, No. 2, pp.232–244, April 2016.

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10. Carolina P. Almeida, Richard A. Goncalves, Elizabeth F. Goldbarg, Marco C. Goldbarg and Myriam R. Delgado, “Anexperimental analysis of evolutionary heuristics for the biobjective traveling purchaser problem”, Annals of OperationsResearch, Vol. 199, No. 1, pp. 305–341, October 2012.

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1. Xianpeng Wang and Lixin Tang, “An adaptive multi-population differential evolution algorithm for continuous multi-objective optimization”, Information Sciences, Vol. 348, pp. 124–141, June 20, 2016.

2. Elena-Niculina Dragoi and Vlad Dafinescu, “Parameter control and hybridization techniques in differential evolution: asurvey”, Artificial Intelligence Review, Vol. 45, No. 4, pp. 447–470, April 2016.

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4. Bili Chen, Wenhua Zeng, Yangbin Lin and Defu Zhang, “A New Local Search-Based Multiobjective OptimizationAlgorithm”, IEEE Transactions on Evolutionary Computation, Vol. 19, No. 1, pp. 50–73, February 2015.

5. Minggang Dong, Ning Wang, Xiaohui Cheng and Chuanxian Jiang, “Composite Differential Evolution with Modified Or-acle Penalty Method for Constrained Optimization Problems”, Mathematical Problems in Engineering, Article Number:617905, 2014.

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12. Swagatam Das and Ponnuthurai Nagaratnam Suganthan, “Differential Evolution: A Survey of the State-of-the-Art”,IEEE Transactions on Evolutionary Computation, Vol. 15, No. 1, pp. 27–54, February 2011.

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1. Noha M. Hamza, Ruhul A. Sarker, Daryl L. Essam, Kalyanmoy Deb and Saber M. Elsayed, “A constraint consensusmemetic algorithm for solving constrained optimization problems”, Engineering Optimization, Vol. 46, No. 11, pp.1447–1464, November 2014.

2. Andrea Maesani, Giovanni Iacca and Dario Floreano, “Memetic Viability Evolution for Constrained Optimization”,IEEE Transactions on Evolutionary Computation, Vol. 20, No. 1, pp. 125–144, February 2016.

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5. Xiangtao Li and Minghao Yin, “Self-adaptive constrained artificial bee colony for constrained numerical optimization”,Neural Computing & Applications, Vol. 24, Nos. 3-4, pp. 723–734, March 2014.

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1. Jiancheng Long, W.Y. Szeto and Hai-Jun Huang, “A bi-objective turning restriction design problem in urban roadnetworks”, European Journal of Operational Research, Vol. 237, No. 2, pp. 426–439, September 1, 2014.

2. Gilberto Reynoso-Meza, Javier Sanchis, Xavier Blasco and Sergio Garcia-Nieto, “Physical programming for preferencedriven evolutionary multi-objective optimization”, Applied Soft Computing, Vol. 24, pp. 341–362, November 2014.

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4. Huanlai Xing and Rong Qu, “A nondominated sorting genetic algorithm for bi-objective network coding based multicastrouting problems”, Information Sciences, Vol. 233, pp. 36–53, June 1, 2013.

5. Dunwei Gong, Jing Sun and Xinfang Ji, “Evolutionary algorithms with preference polyhedron for interval multi-objectiveoptimization problems”, Information Sciences, Vol. 233, pp. 141–161, June 1, 2013.

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8. Liang Huang, Il Hong Suh and Ajith Abraham, “Dynamic multi-objective optimization based on membrane computingfor control of time-varying unstable plants”, Information Sciences, Vol. 181, No. 11, pp. 2370–2391, June 1, 2011.

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• Daniel Cortes Rivera, Ricardo Landa Becerra and Carlos A. Coello Coello, “Cultural Algorithms, an Alter-native Heuristic to Solve the Job Shop Scheduling Problem”, Engineering Optimization, Vol. 39, No. 1, pp.69–85, January 2007.

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2. Binchao Chen and Timothy I. Matis, “A flexible dispatching rule for minimizing tardiness in job shop scheduling”,International Journal of Production Economics, Vol. 141, No. 1, pp. 360–365, January 2013.

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4. Weiling Wang and Tieke Li, “Improved Cultural Algorithms for Job Shop Scheduling Problem”, International Journalof Industrial Engineering–Theory Applications and Practice, Vol. 18, No. 4, pp. 162–168, 2011.

5. Rui Zhang and Cheng Wu, “A divide-and-conquer strategy with particle swarm optimization for the job shop schedulingproblem”, Engineering Optimization, Vol. 42, No. 7, pp. 641–670, 2010.

• Pablo E. Onate Yumbla, Juan M. Ramirez and Carlos A. Coello Coello, “Optimal power flow subject tosecurity constraints solved with a particle swarm optimizer”, IEEE Transactions on Power Systems, Vol. 23,No. 1, pp. 33–40, February 2008.

1. Ming Niu, Can Wan and Zhao Xu, “A review on applications of heuristic optimization algorithms for optimal power flowin modern power systems”, Journal of Modern Power Systems and Clean Energy, Vol. 2, No. 4, pp. 289–297, December2014.

2. Dzung T. Phan and Xu Andy Sun, “Minimal Impact Corrective Actions in Security-Constrained Optimal Power FlowVia Sparsity Regularization”, IEEE Transactions on Power Systems, Vol. 30, No. 4, pp. 1947–1956, July 2015.

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21. A.F. Zobaa and A. Lecci, “Particle swarm optimisation of resonant controller parameters for power converters”, IETPower Electronics, Vol. 4, No. 2, pp. 235–241, 2011.

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3. Ali Asghar Heidari, Rahim Ali Abbaspour and Ahmad Rezaee Jordehi, “An efficient chaotic water cycle algorithm foroptimization tasks”, Neural Computing & Applications, Vol. 28, No. 1, pp. 57–85, January 2017.

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64. Hamdy A. El-Ghandour and Emad Elbeltagi, “Optimal Groundwater Management Using Multiobjective Particle Swarmwith a New Evolution Strategy”, Journal of Hydrologic Engineering, Vol. 19, No. 6, pp. 1141–1149, June 1, 2014.

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70. Kangning Huang, Xiaoping Liu, Xia Li, Jiayong Liang and Shenjing He, “An improved artificial immune system forseeking the Pareto front of land-use allocation problem in large areas”, International Journal of Geographical InformationScience, Vol. 27, No. 5, pp. 922–946, May 1, 2013.

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76. Mengqi Hu, Jeffery D. Weir and Teresa Wu, “An augmented multi-objective particle swarm optimizer for building clusteroperation decisions”, Applied Soft Computing, Vol. 25, pp. 347–359, December 2014.

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78. Maria Dominguez, Antonio Fernandez-Cardador, Asuncion P. Cucala, Tad Gonsalves and Adrian Fernandez, “Multiobjective particle swarm optimization algorithm for the design of efficient ATO speed profiles in metro lines”, EngineeringApplications of Artificial Intelligence, Vol. 29, pp. 43–53, March 2014.

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82. M. Taheri, M.R. Alavi Moghaddam and M. Arami, “Techno-economical optimization of Reactive Blue 19 removal bycombined electrocoagulation/coagulation process through MOPSO using RSM and ANFIS models”, Journal of Envi-ronmental Management, Vol. 128, pp. 798–806, October 15, 2013.

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112. Sajad Tabatabaei, “A new gravitational search optimization algorithm to solve single and multiobjective optimizationproblems”, Journal of Intelligent & Fuzzy Systems, Vol. 26, No. 2, pp. 993–1006, 2014.

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2. Jixin Wang, Wanghao Shen, Zhongda Wang, Mingyao Yao and Xiaohua Zeng, “Multi-objective optimization of drivegears for power split device using surrogate models”, Journal of Mechanical Science and Technology, Vol. 28, No. 6, pp.2205–2214, June 2014.

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13. Anabel Martinez-Vargas, Josue Dominguez-Guerrero, Angel G. Andrade, Roberto Sepulveda and Oscar Montiel-Ross,“Application of NSGA-II algorithm to the spectrum assignment problem in spectrum sharing networks”, Applied SoftComputing, Vol. 39, pp. 188–198, February 2016.

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15. Monalisa Mandal and Anirban Mukhopadhyay, “A Graph-Theoretic Approach for Identifying Non-Redundant and Rele-vant Gene Markers from Microarray Data Using Multiobjective Binary PSO”, Plos One, Vol. 9, No. 3, Article Number:e90949, March 13, 2014.

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39. Shan Cheng, Min-you Chen, Rong-jong Wai and Fang-zong Wang, “Optimal placement of distributed generation unitsin distribution systems via an enhanced multi-objective particle swarm optimization algorithm”, Journal of ZhejiangUniversity–Science C–Computers & Electronics, Vol. 15, No. 4, pp. 300–311, April 2014.

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41. Yu-Jun Zheng, Hai-Feng Ling, Jin-Yun Xue and Sheng-Yong Chen, “Population Classification in Fire Evacuation: AMultiobjective Particle Swarm Optimization Approach”, IEEE Transactions on Evolutionary Computation, Vol. 18, No.1, pp. 70–81, February 2014.

42. Kalyanmoy Deb and Nikhil Padhye, “Enhancing performance of particle swarm optimization through an algorithmiclink with genetic algorithms”, Computational Optimization and Applications, Vol. 57, No. 3, pp. 761–794, April 2014.

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44. Roman Stryczek and Boguslaw Pytlak, “Multi-Objective Optimization with Adjusted PSO Method on Example ofCutting Process of Hardened 18CrMo4 Steel”, Eksploatacja Niezawodnosc–Maintenance and Reliability, No. 1, pp.236–245, 2014.

45. Zhi-Hui Zhan, Jingjing Li, Jiannong Cao, Jun Zhang, Henry Shu-Hung Chung and Yu-Hui Shi, “Multiple Popula-tions for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems”, IEEETransactions on Cybernetics, Vol. 43, No. 2, pp. 445–463, April 2013.

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47. Hu Xia, Jian Zhuang and Dehong Yu, “Combining Crowding Estimation in Objective and Decision Space With MultipleSelection and Search Strategies for Multi-Objective Evolutionary Optimization”, IEEE Transactions on Cybernetics,Vol. 44, No. 3, pp. 378–393, March 2014.

48. Andre Britto and Aurora Pozo, “Using reference points to update the archive of MOPSO algorithms in Many-ObjectiveOptimization”, Neurocomputing, Vol. 127, pp. 78–87, March 15, 2014.

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58. Martin Pilat and Roman Neruda, “Aggregate meta-models for evolutionary multiobjective and many-objective opti-mization”, Neurocomputing, Vol. 116, pp. 392–402, September 20, 2013.

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62. Siwadol Kanyakam and Sujin Bureerat, “Multiobjective Optimization of a Pin-Fin Heat Sink Using Evolutionary Algo-rithms”, Journal of Electronic Packaging, Vol. 134, No. 2, Article Number: 021008, June 2012.

63. Gang Lei, X.M. Chen, J.G. Zhu, Y.G. Guo, Wei Xu and K.R. Shao, “Multiobjective Sequential Optimization Method forthe Design of Industrial Electromagnetic Devices”, IEEE Transactions on Magnetics, Vol. 48, No. 11, pp. 4538–4541,November 2012.

64. Vigneshwaran Namasivayam and Jurgen Bajorath, “Multiobjective Particle Swarm Optimization: Automated Identifi-cation of Structure-Activity Relationship-Informative Compounds with Favorable Physicochemical Property Distribu-tions”, Journal of Chemical Information and Modeling, Vol. 52, No. 11, pp. 2848–2855, November 2012.

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69. Lixin Tang and Xianpen Wang, “A Hybrid Multiobjective Evolutionary Algorithm for Multiobjective OptimizationProblems”, IEEE Transactions on Evolutionary Computation, Vol. 17, No. 1, pp. 20–45, February 2013.

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76. Lie-Jane Kao and Cheng-Few Lee, “Alternative method for determining industrial bond ratings: theory and empiricalevidence”, International Journal of Information Technology & Decision Making, Vol. 11, No. 6, pp. 1215–1235,November 2012.

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10. Harish Garg, “Solving Structural Engineering Design Optimization Problems using an Artificial Bee Colony Algorithm”,Journal of Industrial and Management Optimization, Vol. 10, No. 3, pp. 777–794, July 2014.

11. Saber M. Elsayed, Ruhul A. Sarker and Daryl L. Essam, “ Adaptive Configuration of evolutionary algorithms forconstrained optimization”, Applied Mathematics and Computation, Vol. 222, pp. 680–711, October 1, 2013.

12. A. Kaveh and S. Talatahari, “Hybrid charged system search and particle swarm optimization for engineering designproblems”, Engineering Computations, Vol. 28, Nos. 3-4, pp. 423–440, 2011.

13. A. Kaveh, Mohammad A. Motie Share and M. Moslehi, “Magnetic charged system search: a new meta-heuristic algorithmfor optimization”, Acta Mechanica, Vol. 224, No. 1, pp. 85–107, January 2013.

14. Amir Hossein Gandomi, Xin-She Yang, Amir Hossein Alavi and Siamak Talatahari, “Bat algorithm for constrainedoptimization tasks”, Neural Computing & Applications, Vol. 22, No. 6, pp. 1239–1255, May 2013.

15. Ali Sadollah, Ardeshir Bahreininejad, Hadi Eskandar and Mohd Hamdi, “Mine blast algorithm: A new population basedalgorithm for solving constrained engineering optimization problems”, Applied Soft Computing, Vol. 13, No. 5, pp.2592–2612, May 2013.

16. A. Kaveh and M. Ahangaran, “Social Harmony Search Algorithm for Continuous Optimization”, Iranian Journal ofScience and Technology-Transactions of Civil Engineering, Vol. 36, No. C2, pp. 121–137, August 2012.

17. Hadi Eskandar, Ali Sadollah, Ardeshir Bahreininejad and Mohd Hamdi, “Water cycle algorithm - A novel metaheuristicoptimization method for solving constrained engineering optimization problems”, Computers & Structures, Vol. 110, pp.151–166, November 2012.

18. Saber M. Elsayed, Ruhul A. Sarker and Daryl L. Essam, “On an evolutionary approach for constrained optimizationproblem solving”, Applied Soft Computing, Vol. 12, No. 10, pp. 3208–3227, October 2012.

19. S.O. Degertekin, “Improved harmony search algorithms for sizing optimization of truss structures”, Computers & Struc-tures, Vol. 92-93, pp. 229–241, February 2012.

20. Ali Husseinzadeh Kashan, “An efficient algorithm for constrained global optimization and application to mechanicalengineering design: League championship algorithm (LCA)”, Computer-Aided Design, Vol. 43, No. 12, pp. 1769–1792,December 2011.

21. Shih-Cheng Horng, Shieh-Shing Lin and Feng-Yi Yang, “Evolutionary algorithm for stochastic job shop scheduling withrandom processing time”, Expert Systems with Applications, Vol. 39, No. 3, pp. 3603–3610, February 15, 2012.

22. A. Kaveh and S. Talatahari, “An improved ant colony optimization for constrained engineering design problems”,Engineering Computations, Vol. 27, Nos. 1-2, pp. 155–182, 2010.

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23. A. Kaveh and S. Talatahari, “A novel heuristic optimization method: charged system search”, Acta Mechanica, Vol.213, Nos. 3-4, pp. 267–289, September 2010.

24. A. Kaveh and S. Talatahari, “A particle swarm ant colony optimization for truss structures with discrete variables”,Journal of Constructional Steel Research, Vol. 65, Nos. 8–9, pp. 1558–1568, August-September 2009.

Congresos Internacionales

• Raquel Hernandez Gomez and Carlos A. Coello Coello, “Improved Metaheuristic Based on the R2 Indicatorfor Many-Objective Optimization”, in 2015 Genetic and Evolutionary Computation Conference (GECCO2015), pp. 679–686, ACM Press, Madrid, Spain, July 11-15, 2015, ISBN 978-1-4503-3472-3.

1. Yiping Liu, Dunwei Gong, Jing Sun and Yaochu Jin, “A Many-Objective Evolutionary Algorithm Using A One-by-OneSelection Strategy”, IEEE Transactions on Cybernetics, Vol. 47, No. 9, pp. 2689–2702, September 2017.

2. Yi Xiang, Yuren Zhou, Miqing Li and Zefeng Chen, “A Vector Angle-Based Evolutionary Algorithm for UnconstrainedMany-Objective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 21, No. 1, pp. 131–152, Febru-ary 2017.

• Jorge S. Hernandez Domınguez, Gregorio Toscano Pulido and Carlos A. Coello Coello, “A Multi-ObjectiveParticle Swarm Optimizer Enhanced with a Differential Evolution Scheme”, in Jin-Kao Hao, Pierrick Legrand,Pierre Collet, Nicolas Monmarche, Evelyne Lutton and Marc Schoenauer (editors), Artificial Evolution,10th International Conference, Evolution Artificielle, EA 2011, pp. 169–180, Springer. Lecture Notes inComputer Science Vol. 7401, Angers, France, October 24-26, 2012.

1. Yi-xin Su and Rui Chi, “Multi-objective particle swarm-differential evolution algorithm”, Neural Computing & Applica-tions, Vol. 28, No. 2, pp. 407–418, February 2017.

• Adriana Menchaca-Mendez and Carlos A. Coello Coello, “GD-MOEA: A New Multi-Objective EvolutionaryAlgorithm based on the Generational Distance Indicator”, in Antonio Gaspar-Cunha, Carlos HenggelerAntunes and Carlos Coello Coello (Editors), Evolutionary Multi-Criterion Optimization, 8th InternationalConference, EMO 2015, pp. 156–170, Springer. Lecture Notes in Computer Science Vol. 9018, Guimaraes,Portugal, March 29 - April 1, 2015.

1. Alan Diaz-Manriquez, Gregorio Toscano, Jose Hugo Barron-Zambrano and Edgar Tello-Leal, “R2-Based Multi/Many-Objective Particle Swarm Optimization”, Computational Intelligence and Neuroscience, Article Number: 1898527, 2016.

• Efren Mezura Montes and Carlos A. Coello Coello, “Identifying On-line Behavior and Some Sources ofDifficulty in Two Competitive Approaches for Constrained Optimization”, in 2005 IEEE Congress on Evo-lutionary Computation (CEC’2005), pp. 1477–1484, IEEE Press, Vol. 2, Edinburgh, Scotland, September2005.

1. Guohua Wu, Witold Pedrycz, P.N. Suganthan and Rammohan Mallipeddi, “A variable reduction strategy for evolutionaryalgorithms handling equality constraints”, Applied Soft Computing, Vol. 37, pp. 774–786, December 2015.

• Carlos A. Coello Coello, Alan D. Christiansen and Arturo Hernandez Aguirre, “Multiobjective Design Op-timization of Counterweight Balancing of a Robot Arm Using Genetic Algorithms”, in Proceedings of theSeventh International Conference on Tools with Artificial Intelligence (TAI’95), pp. 20–23, IEEE ComputerSociety Press, Herndon, Virginia, USA, 5–8 November 1995.

1. Pavel Tomsic and Joze Duhovnik, “Simultaneous Topology and Size Optimization of 2D and 3D Trusses Using Evo-lutionary Structural Optimization with regard to Commonly Used Topologies”, Advances in Mechanical Engineering,Article Number: 864807, 2014.

• Jesus Moises Osorio Velazquez, Carlos A. Coello Coello and Alfredo Arias-Montano, “Multi-Objective Com-pact Differential Evolution”, in 2014 IEEE Symposium on Differential Evolution (part of the 2014 IEEESymposium Series on Computational Intelligence), pp. 49–56, IEEE Press, ISBN 978-1-4799-4462-0, Or-lando, Florida, USA, December 9-12, 2014.

1. Seyedali Mirjalili, Shahrzad Saremi, Seyed Mohammad Mirjalili and Leadro dos S. Coelho, “Multi-objective grey wolfoptimizer: A novel algorithm for multi-criterion optimization”, Expert Systems with Applications, Vol. 47, pp. 106–119,April 1, 2016.

• Antonio J. Nebro, Juan J. Durillo and Carlos A. Coello Coello, “Analysis of Leader Selection Strategiesin a Multi-Objective Particle Swarm Optimizer”, in 2013 IEEE Congress on Evolutionary Computation(CEC’2013), pp. 3153–3160, IEEE Press, Cancun, Mexico, 20-23 June, 2013.

230

1. Seyedali Mirjalili, Shahrzad Saremi, Seyed Mohammad Mirjalili and Leadro dos S. Coelho, “Multi-objective grey wolfoptimizer: A novel algorithm for multi-criterion optimization”, Expert Systems with Applications, Vol. 47, pp. 106–119,April 1, 2016.

• Alejandro Rosales, Jesus A. Gonzalez, Carlos A. Coello Coello, Carlos A. Reyes-Garcıa and Hugo JairEscalante, “Evolutionary Multi-Objective Approach for Prototype Generation and Feature Selection”, inEduardo Bayro-Corrochano and Edwin Hancock (Editors), Progress in Pattern Recognition, Image Analysis,Computer Vision, and Applications, 19th Iberoamerican Congress, CIARP 2014, pp. 424–431, Springer,Lecture Notes in Computer Science Vol. 8827, Puerto Vallarta, Mexico, November 2-5, 2014.

1. Siew Chin Neoh, Li Zhang, Kamlesh Mistry, Mohammed Alamgir Hossain, Chee Peng Lim, Nauman Aslam and PhilipKinghorn, “Intelligent facial emotion recognition using a layered encoding cascade optimization model”, Applied SoftComputing, Vol. 34, pp. 72–93, September 2015.

• Ivan Chaman Garcıa, Carlos A. Coello Coello and Alfredo Arias-Montano, “MOPSOhv: A New Hypervolume-based Multi-Objective Particle Swarm Optimizer”, in 2014 IEEE Congress on Evolutionary Computation(CEC’2014), pp. 266–273, IEEE Press, Beijing, China, 6-11 July 2014, ISBN 978-1-4799-1488-3.

1. Xiaoyan Sun, Yang Chen,Yiping Liu and Dunwei Gong, “Indicator-based set evolution particle swarm optimization formany-objective problems”, Soft Computing, Vol. 20, No. 6, pp. 2219–2232, June 2016.

• Miguel A. Medina, Swagatam Das, Carlos A. Coello Coello and Juan M. Ramirez, “Two Decomposition-based Modern Metaheuristic Algorithms for Multi-objective Optimization - A Comparative Study”, in Pro-ceedings of the 2013 IEEE Symposium on Computational Intelligence in Multicriteria Decision Making(MCDM’2013), pp. 9–16, IEEE Press, Singapore, April 16–19, 2013.

1. Wali Khan Mashwani,Abdellah Salhi, Muhammad Asif Jan, Muhammad Sulaiman, Rashida Adeeb Khanum and Abdul-mohsen Algarni, “Evolutionary Algorithms Based on Decomposition and Indicator Functions: State-of-the-art Survey”,International Journal of Advanced Computer Science and Applications, Vol. 7, No. 2, pp. 583–593, February 2016.

• Miriam Pescador Rojas and Carlos A. Coello Coello, “A Memetic Algorithm with Simplex Crossover forSolving Constrained Optimization Problems”, in Proceedings of 2012 World Automation Congress (WAC2012), Puerto Vallarta, Mexico, TSI Enterprises, Inc., June 24-27, 2012.

1. Vinicius Veloso de Melo and Giovanni Iacca, “A modified Covariance Matrix Adaptation Evolution Strategy with adaptivepenalty function and restart for constrained optimization”, Expert Systems with Applications, Vol. 41, No. 16, pp. 7077–7094, November 15, 2014.

2. Andrea Maesani, Giovanni Iacca and Dario Floreano, “Memetic Viability Evolution for Constrained Optimization”,IEEE Transactions on Evolutionary Computation, Vol. 20, No. 1, pp. 125–144, February 2016.

3. Zubair Rehman, Ibrahim Al-Bahadly and Subhas Mukhopadhyay, “Multiinput DC-DC converters in renewable energyapplications - An overview”, Renewable & Sustainable Energy Reviews, Vol. 41, pp. 521–539, January 2015.

• Adriana Lara, Sergio Alvarado, Shaul Salomon, Gideon Avigad, Carlos A. Coello Coello and Oliver Schutze,“The Gradient Free Directed Search Method as Local Search within Multi-Objective Evolutionary Algo-rithms”, in Oliver Schutze, Carlos A. Coello Coello, Alexandru-Adrian Tantar, Emilia Tantar, Pascal Bou-vry, Pierre Del Moral and Pierrick Legrand (editors), EVOLVE - A Bridge between Probability, Set OrientedNumerics, and Evolutionary Computation II, pp. 153–168, Springer, Advances in Intelligent Systems andComputing Vol. 175, Berlin, Germany, 2012, ISBN 978-3-642-31519-0.

1. Siu Lau Ho, Jiaqiang Yang, Shiyou Yang and Yanan Bai, “Integration of Directed Searches in Particle Swarm Optimiza-tion for Multi-Objective Optimization”, IEEE Transactions on Magnetics, Vol. 51, Article Number: 7000804, March2015.

• Luis V. Santana-Quintero, Vıctor A. Serrano-Hernandez, Carlos A. Coello Coello, Alfredo G. Hernandez-Dıaz and Julian Molina, “Use of Radial Basis Functions and Rough Sets for Evolutionary Multi-ObjectiveOptimization”, in Proceedings of the 2007 IEEE Symposium on Computational Intelligence in MulticriteriaDecision Making (MCDM’2007), pp. 107–114, IEEE Press, Honolulu, Hawaii, USA, April 2007.

1. Taimoor Akhtar and Christine A. Shoemaker, “Multi objective optimization of computationally expensive multi-modalfunctions with RBF surrogates and multi-rule selection”, Journal of Global Optimization, Vol. 64, No. 1, pp. 17–32,January 2016.

231

• Saul Zapotecas Martınez and Carlos A. Coello Coello, “MOEA/D assisted by RBF Networks for Expen-sive Multi-Objective Optimization Problems”, in 2013 Genetic and Evolutionary Computation Conference(GECCO’2013), pp. 1405–1412, ACM Press, New York, USA, July 6-10, 2013, ISBN 978-1-4503-1963-8.

1. Handing Wang, Yaochu Jin and Jan O. Jansen, “Data-Driven Surrogate-Assisted Multiobjective Evolutionary Optimiza-tion of a Trauma System”, IEEE Transactions on Evolutionary Computation, Vol. 20, No. 6, pp. 939–952, December2016.

2. Taimoor Akhtar and Christine A. Shoemaker, “Multi objective optimization of computationally expensive multi-modalfunctions with RBF surrogates and multi-rule selection”, Journal of Global Optimization, Vol. 64, No. 1, pp. 17–32,January 2016.

• Saul Zapotecas Martınez and Carlos A. Coello Coello, “Combining Surrogate Models and Local Search forDealing with Expensive Multi-objective Optimization Problems”, in 2013 IEEE Congress on EvolutionaryComputation (CEC’2013), pp. 2572–2579, IEEE Press, Cancun, Mexico, 20-23 June, 2013.

1. Taimoor Akhtar and Christine A. Shoemaker, “Multi objective optimization of computationally expensive multi-modalfunctions with RBF surrogates and multi-rule selection”, Journal of Global Optimization, Vol. 64, No. 1, pp. 17–32,January 2016.

• Carlos Segura, Carlos A. Coello Coello, Eduardo Segredo, Gara Miranda and Coromoto Leon, “Improvingthe Diversity Preservation of Multi-objective Approaches used for Single-objective Optimization”, in 2013IEEE Congress on Evolutionary Computation (CEC’2013), pp. 3198–3205, IEEE Press, Cancun, Mexico,20-23 June, 2013.

1. Simon Wessing and Mike Preuss, “On multiobjective selection for multimodal optimization”, Computational Optimiza-tion and Applications, Vol. 63, No. 3, pp. 875–902, April 2016.

2. Wei-Fang Gao, Gary G. Yen and San-Yang Liu, “A Dual-Population Differential Evolution with Coevolution for Con-strained Optimization”, IEEE Transactions on Cybernetics, Vol. 45, No. 5, pp. 1094–1107, May 2015.

• Alejandro Rosales-Perez, Carlos A. Coello Coello, Jesus A. Gonzalez, Carlos A. Reyes-Garcıa and Hugo JairEscalante, “A Hybrid Surrogate-Based Approach for Evolutionary Multi-Objective Optimization”, in 2013IEEE Congress on Evolutionary Computation (CEC’2013), pp. 2548–2555, IEEE Press, Cancun, Mexico,20-23 June, 2013.

1. Taimoor Akhtar and Christine A. Shoemaker, “Multi objective optimization of computationally expensive multi-modalfunctions with RBF surrogates and multi-rule selection”, Journal of Global Optimization, Vol. 64, No. 1, pp. 17–32,January 2016.

2. Lucas M. Pavelski, Myriam R. Delgado, Carolina P. Almeida, Richard A. Goncalves and Sandra M. Venske, “ExtremeLearning Surrogate Models in Multi-objective Optimization based on Decomposition”, Neurocomputing, Vol. 180, pp.55–67, March 5, 2016.

• Adriana Menchaca-Mendez and Carlos A. Coello Coello, “A New Selection Mechanism Based on Hyper-volume and its Locality Property”, in 2013 IEEE Congress on Evolutionary Computation (CEC’2013), pp.924–931, IEEE Press, Cancun, Mexico, 20-23 June, 2013.

1. Mohammad Abbasi Rad and Ali Hamzeh, “A coevolutionary approach to many objective optimization based on a novelranking method”, Intelligent Data Analysis, Vol. 20, No. 1, pp. 129–151, 2016.

• M. Davarynejad, M.-R. Akbarzadeh-T and Carlos A. Coello Coello, “Auto-Tuning Fuzzy Granulation forEvolutionary Optimization”, in 2008 Congress on Evolutionary Computation (CEC’2008), pp. 3573–3580,IEEE Service Center, Hong Kong, June 2008.

1. Zahra Pourbahman and Ali Hamzeh, “A fuzzy based approach for fitness approximation in multi-objective evolutionaryalgorithms”, Journal of Intelligent & Fuzzy Systems, Vol. 29, No. 5, pp. 2111–2131, 2015.

• Mohsen Davarynejad, Jafar Rezaei, Jos Vrancken, Jan van den Berg and Carlos A. Coello Coello, “Accelerat-ing Convergence Towards the Optimal Pareto Front”, in 2011 IEEE Congress on Evolutionary Computation(CEC’2011), pp. 2107–2114, IEEE Service Center, New Orleans, Louisiana, USA, 5-8 June, 2011.

1. Zahra Pourbahman and Ali Hamzeh, “A fuzzy based approach for fitness approximation in multi-objective evolutionaryalgorithms”, Journal of Intelligent & Fuzzy Systems, Vol. 29, No. 5, pp. 2111–2131, 2015.

232

• Adriana Menchaca-Mendez and Carlos A. Coello Coello, “Solving Multi-Objective Optimization Problemsusing Differential Evolution and a Maximin Selection Criterion”, in 2012 IEEE Congress on EvolutionaryComputation (CEC’2012), pp. 3143–3150, IEEE Press, Brisbane, Australia, June 10-15, 2012.

1. Bingdong Li, Jinlong Li, Ke Tang and Xin Yao, “Many-Objective Evolutionary Algorithms: A Survey”, ACM ComputingSurveys, Vol. 48, No. 1, Article Number: 13, September 2015.

• Adriana Menchaca-Mendez and Carlos A. Coello Coello, “Selection Operators Based on Maximin FitnessFunction for Multi-Objective Evolutionary Algorithms”, in Robin C. Purshouse, Peter J. Fleming, CarlosM. Fonseca, Salvatore Greco and Jane Shaw (editors), Evolutionary Multi-Criterion Optimization, 7th In-ternational Conference, EMO 2013, pp. 215–229, Springer. Lecture Notes in Computer Science Vol. 7811,Sheffield, UK, March 19-22, 2013.

1. Bingdong Li, Jinlong Li, Ke Tang and Xin Yao, “Many-Objective Evolutionary Algorithms: A Survey”, ACM ComputingSurveys, Vol. 48, No. 1, Article Number: 13, September 2015.

• Alan Dıaz-Manrıquez, Gregorio Toscano-Pulido, Carlos A. Coello Coello and Ricardo Landa-Becerra, “ARanking Method Based on the R2 indicator for Many-Objective Optimization”, in 2013 IEEE Congress onEvolutionary Computation (CEC’2013), pp. 1523–1530, IEEE Press, Cancun, Mexico, 20-23 June, 2013.

1. Miqing Li, Shengxiang Yang and Xiaohui Liu, “Pareto or Non-Pareto: Bi-Criterion Evolution in Multiobjective Opti-mization”, IEEE Transactions on Evolutionary Computation, Vol. 20, No. 5, pp. 645–665, October 2016.

2. Bingdong Li, Jinlong Li, Ke Tang and Xin Yao, “Many-Objective Evolutionary Algorithms: A Survey”, ACM ComputingSurveys, Vol. 48, No. 1, Article Number: 13, September 2015.

• Ricardo Landa, Carlos A. Coello Coello and Gregorio Toscano-Pulido, “Goal-constraint: Incorporating Pref-erences Through an Evolutionary ε-constraint Based Method”, in 2013 IEEE Congress on EvolutionaryComputation (CEC’2013), pp. 741–747, IEEE Press, Cancun, Mexico, 20-23 June, 2013.

1. Bingdong Li, Jinlong Li, Ke Tang and Xin Yao, “Many-Objective Evolutionary Algorithms: A Survey”, ACM ComputingSurveys, Vol. 48, No. 1, Article Number: 13, September 2015.

• Hiroyuki Sato, Carlos A. Coello Coello, Hernan E. Aguirre and Kiyoshi Tanaka, “Adaptive Control of theNumber of Crossed Genes in Many-Objective Evolutionary Optimization”, in Youssef Hamadi and MarcSchoenauer (editors), Learning and Intelligent Optimization, 6th International Conference, LION 6, pp.478–484, Springer, Lecture Notes in Computer Science Vol. 7219, Paris, France, January 2012, ISBN 978-3-642-34413-8.

1. Bingdong Li, Jinlong Li, Ke Tang and Xin Yao, “Many-Objective Evolutionary Algorithms: A Survey”, ACM ComputingSurveys, Vol. 48, No. 1, Article Number: 13, September 2015.

2. Wiem Mkaouer, Marouane Kessentini, Adnan Shaout, Patrice Koligheu, Slim Bechikh, Kalyanmoy Deb and Ali Ouni,“Many-Objective Software Remodularization Using NSGA-III”, ACM Transactions on Software Engineering and Method-ology, Vol. 24, No. 3, Article Number: 17, May 2015.

• Raquel Hernandez Gomez and Carlos A. Coello Coello, “MOMBI: A New Metaheuristic for Many-ObjectiveOptimization Based on the R2 Indicator”, in 2013 IEEE Congress on Evolutionary Computation (CEC’2013),pp. 2488–2495, IEEE Press, Cancun, Mexico, 20-23 June, 2013.

1. Yuan Yuan, Hua Xu, Bo Wang, Bo Zhang and Xin Yao, “Balancing Convergence and Diversity in Decomposition-BasedMany-Objective Optimizers”, IEEE Transactions on Evolutionary Computation, Vol. 20, No. 2, pp. 180–198, April2016.

2. Yuan Yuan, Hua Xu, Bo Wang and Xin Yao, “A New Dominance Relation-Based Evolutionary Algorithm for Many-Objective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 20, No. 1, pp. 16–37, February2016.

3. Lei Cai, Shiru Qu, Yuan Yuan and Xin Yao, “A clustering-ranking method for many-objective optimization”, AppliedSoft Computing, Vol. 35, pp. 681–694, October 2015.

4. Sanghamitra Bandyopadhyay and Arpan Mukherjee, “An Algorithm for Many-Objective Optimization with ReducedObjective Computations: A Study in Differential Evolution”, IEEE Transactions on Evolutionary Computation, Vol.19, No. 3, pp. 400–413, June 2015.

• Saul Zapotecas Martınez and Carlos A. Coello Coello, “A Hybridization of MOEA/D with the NonlinearSimplex Search Algorithm”, in Proceedings of the 2013 IEEE Symposium on Computational Intelligence inMulticriteria Decision Making (MCDM’2013), pp. 48–55, IEEE Press, Singapore, April 16–19, 2013.

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1. Wali Khan Mashwani,Abdellah Salhi, Muhammad Asif Jan, Muhammad Sulaiman, Rashida Adeeb Khanum and Abdul-mohsen Algarni, “Evolutionary Algorithms Based on Decomposition and Indicator Functions: State-of-the-art Survey”,International Journal of Advanced Computer Science and Applications, Vol. 7, No. 2, pp. 583–593, February 2016.

2. Krzysztof Michalak, “Using an outward selective pressure for improving the search quality of the MOEA/D algorithm”,Computational Optimization and Applications, Vol. 61, No. 3, pp. 571–607, July 2015.

• Antonio Lopez, Carlos A. Coello Coello, Akira Oyama and Kozo Fujii, “An Alternative Preference Relationto Deal with Many-Objective Optimization Problems”, in Robin C. Purshouse, Peter J. Fleming, Carlos M.Fonseca, Salvatore Greco and Jane Shaw (editors), Evolutionary Multi-Criterion Optimization, 7th Inter-national Conference, EMO 2013, pp. 291–306, Springer. Lecture Notes in Computer Science Vol. 7811,Sheffield, UK, March 19-22, 2013.

1. Ran Cheng, Yaochu Jin, Markus Olhofer and Bernhard Sendhoff, “A Reference Vector Guided Evolutionary Algorithmfor Many-Objective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 20, No. 5, pp. 773–791,October 2016.

2. Chang Luo, Koji Shimoyama and Shigeru Obayashi, “A Study on Many-Objective Optimization Using the Kriging-Surrogate-Based Evolutionary Algorithm Maximizing Expected Hypervolume Improvement”, Mathematical Problems inEngineering, Article Number: 162712, 2015.

3. Yuan Yuan, Hua Xu, Bo Wang and Xin Yao, “A New Dominance Relation-Based Evolutionary Algorithm for Many-Objective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 20, No. 1, pp. 16–37, February2016.

4. Xingyi Zhang, Ye Tian anc Yaochu Jin, “A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimiza-tion”, IEEE Transactions on Evolutionary Computation, Vol. 19, No. 6, pp. 761–776, December 2015.

5. Bingdong Li, Jinlong Li, Ke Tang and Xin Yao, “Many-Objective Evolutionary Algorithms: A Survey”, ACM ComputingSurveys, Vol. 48, No. 1, Article Number: 13, September 2015.

6. Helio Freire, P.B. de Moura Oliveira, E.J. Solteiro Pires and Maximino Bessa, “Many-objective optimization with corner-based search”, Memetic Computing, Vol. 7, No. 2, pp. 105–118, June 2015.

• Nareli Cruz-Cortes, Francisco Rodrıguez Henrıquez, Raul Juarez-Morales and Carlos A. Coello Coello, “Find-ing Optimal Addition Chains Using a Genetic Algorithm Approach”, in Yue Hao et al. (editors), Computa-tional Intelligence and Security. International Conference, CIS 2005, pp. 208–215, Part I, Springer-Verlag,Lecture Notes in Artificial Intelligence Vol. 3801, Xi’an, China, December 2005.

1. Saul Dominguez-Isidro, Efren Mezura-Montes and Luis-Guillermo Osorio-Hernandez, “Evolutionary programming forthe length minimization of addition chains”, Engineering Applications of Artificial Intelligence, Vol. 37, pp. 125–134,January 2015.

• Guillermo Leguizamon and Carlos A. Coello Coello, “An alternative ACOR algorithm for continuous opti-mization problems”, in Marco Dorigo et al. (editors), Swarm Intelligence, 7th International Conference,ANTS 2010, pp. 48–59, Springer, Lecture Notes in Computer Science Vol. 6234, Brussels, Belgium, Septem-ber 2010.

1. Ahmed Marwan, Meng-Meng Zhou, M. Zaki Abdelrehim and Gunther Meschke, “Optimization of artificial groundfreezing in tunneling in the presence of seepage flow”, Computers and Geotechnics, Vol. 75, pp. 112–125, May 2016.

2. Imad El Fachtali, Rachid Saadane and Mohammed ElKoutbi, “Vertical Handover Decision Algorithm Using Ants’Colonies for 4G Heterogeneous Wireless Networks”, Journal of Computer Networks and Communications, Article Num-ber: 6259802, 2016.

3. Reza Shamsaee, Mahmood Fathy and Ali Masoudi-Nejad, “Extracting a cancer model by enhanced ant colony optimi-sation algorithm”. International Journal of Data Mining and Bioinformatics, Vol. 10, No. 1, pp. 83–97, 2014.

4. Tianjun Liao, Thomas Stutzle, Marco A. Montes de Oca and Marco Dorigo, “A unified ant colony optimization algorithmfor continuous optimization”, European Journal of Operational Research, Vol. 234, No. 3, pp. 597–609, May 1, 2014.

• Saul Zapotecas Martınez and Carlos A. Coello Coello, “A Memetic Algorithm with Non Gradient-BasedLocal Search Assisted by a Meta-Model”, in Robert Schaefer, Carlos Cotta, Joanna Kolodziej and GunterRudolph (editors), Parallel Problem Solving from Nature–PPSN XI, 11th International Conference, Part I,pp. 576–585, Springer, Lecture Notes in Computer Science Vol. 6238, Krakow, Poland, September 2010.

1. Taimoor Akhtar and Christine A. Shoemaker, “Multi objective optimization of computationally expensive multi-modalfunctions with RBF surrogates and multi-rule selection”, Journal of Global Optimization, Vol. 64, No. 1, pp. 17–32,January 2016.

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2. Yaochu Jin, “Surrogate-assisted evolutionary computation: Recent advances and future challenges”, Swarm and Evolu-tionary Computation, Vol. 1, No. 2, pp. 61–70, June 2011.

• Emilia Tantar, Oliver Schutze, Jose Rui Figueira, Carlos A. Coello Coello and El-Ghazali Talbi, “Comput-ing and Selecting ε-Efficient Solutions of {0,1}-Knapsack Problems”, in Matthias Ehrgott, Boris Naujoks,Theodor J. Stewart and Jyrki Wallenius (editors), Multiple Criteria Decision Making for Sustainable Energyand Transportation Systems, pp. 379–389, Springer, Lecture Notes in Economics and Mathematical SystemsVol. 634, Heidelberg, Germany, 2010.

1. Hu Xia, Jian Zhuang and Dehong Yu, “Multi-objective unsupervised feature selection algorithm utilizing redundancymeasure and negative epsilon-dominance for fault diagnosis”, Neurocomputing, Bol. 146, pp. 113–124, December 25,2014.

• Oliver Schutze, Carlos Coello Coello and El-Ghazali Talbi, “Approximating the ε-Efficient Set of an MOPwith Stochastic Search Algorithms”, in Alexander Gelbukh and Angel Fernando Kuri Morales (editors),MICAI 2007: Advances in Artificial Intelligence, 6th International Conference on Artificial Intelligence,pp. 128–138, Springer, Lecture Notes in Artificial Intelligence Vol. 4827, Aguascalientes, Mexico, November2007.

1. Hu Xia, Jian Zhuang and Dehong Yu, “Multi-objective unsupervised feature selection algorithm utilizing redundancymeasure and negative epsilon-dominance for fault diagnosis”, Neurocomputing, Vol. 146, pp. 113–124, December 25,2014.

• Antonio Lopez-Jaimes, Alfredo Arias-Montano and Carlos A. Coello Coello, “Preference Incorporation toSolve Many-Objective Airfoil Design Problems”, in 2011 IEEE Congress on Evolutionary Computation(CEC’2011), pp. 1605–1612, IEEE Service Center, New Orleans, Louisiana, USA, 5-8 June, 2011.

1. Jurgen Branke, Salvatore Corrente, Salvatore Greco, Roman Slowinski and Piotr Zielniewicz, “Using Choquet integralas preference model in interactive evolutionary multiobjective optimization”, European Journal of Operational Research,Vol. 250, No. 3, pp. 884–901, May 1, 2016.

2. Bingdong Li, Jinlong Li, Ke Tang and Xin Yao, “Many-Objective Evolutionary Algorithms: A Survey”, ACM ComputingSurveys, Vol. 48, No. 1, Article Number: 13, September 2015.

3. Eduardo Fernandez, Claudia Gomez, Gilberto Rivera and Laura Cruz-Reyes, “Hybrid metaheuristic approach for han-dling many objectives and decisions on partial support in project portfolio optimisation”, Information Sciences, Vol.315, pp. 102–122, September 10, 2015.

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