1. Can we steal the techniques usedin nature to solve
problems?
2. Introduction toEvolutionary Algorithms (and open
questions)Herb Susmann Computer Science 14Advisor: Dr. Gregg
Hartvigsen 3. In Three Parts: IntroductionExamples My Research 4.
WHAT IS A GENETICALGORITHM? 5. A genetic algorithm is a
biologically inspired optimization algorithm. 6. Cultural
AlgorithmSwarm AlgorithmsMemetic Algorithm Particle Swarm
OptimizationProbabilistic AlgorithmsAnt SystemPopulation-Based
Incremental Learning Ant Colony SystemUnivariate Marginal
Distribution AlgorithmBees AlgorithmCompact Genetic Algorithm
Bacterial Foraging Optimization AlgorithmBayesian Optimization
AlgorithmCross-Entropy MethodImmune AlgorithmsClonal Selection
AlgorithmEvolutionary Algorithms Negative Selection
AlgorithmGenetic Algorithm Artificial Immune Recognition
SystemGenetic Programming Immune Network AlgorithmEvolution
StrategiesDendritic Cell AlgorithmDifferential
EvolutionEvolutionary ProgrammingNeural AlgorithmsGrammatical
Evolution PerceptronGene Expression Programming
Back-propagationLearning Classifier SystemHopfield
NetworkNon-dominated Sorting Genetic Algorithm Learning Vector
QuantizationStrength Pareto Evolutionary AlgorithmSelf-Organizing
Map Source: cleveralgorithms.com 7. Biological Evolution:Natural
SelectionGenetic Recombination & Mutation 8. How can we model
this in a computer? 9. A very informal description:1. Generate a
population of random individuals2. Kill off the worst individuals
in the populationSelection Pressure3. Let the good individuals
mutate and recombine to replace the bad onesGeneticRecombination4.
Repeat until ending criteria met 10. A very informal
example:Evolving the colorBLUE 11. Red Green Blue209 232 3522882062
189 28117 187 40204 28225184 156 1762770161174 171 176 12. Red
Green Blue209 232 3522882062 189 28117 187 40204 28225184 156
1762770161174 171 176 13. Red Green
Blue22881612288206278820627701612788161228816127701612788161 14.
Red Green Blue2288161MUTATE22288206Adaption Red
-20278820627701612788161MUTATE2211988161DeleteriousGreen
+3127701612788161 15. Example:DATA FITTING 16. Growth RateCarrying
Capacity 17. Starting Population Size 18. How can we rank them? Sum
of the error squared 19. Free RCodeHartvigsens
OutboxfilesOutBoxBiologyhartvigShared Learning in ScienceMy
Websitehttp://herbsusmann.com 20. DEMONSTRATION 21. Final
Note:There are much better algorithms to do this. 22. MY RESEARCH
23. Evolve Mathematical Disease Models to fit Data 24. Susceptible
InfectiousRecovered 25. Can we go the other direction? 26. Cultural
AlgorithmSwarm AlgorithmsMemetic Algorithm Particle Swarm
OptimizationProbabilistic AlgorithmsAnt SystemPopulation-Based
Incremental Learning Ant Colony SystemUnivariate Marginal
Distribution AlgorithmBees AlgorithmCompact Genetic Algorithm
Bacterial Foraging Optimization AlgorithmBayesian Optimization
AlgorithmCross-Entropy MethodImmune AlgorithmsClonal Selection
AlgorithmEvolutionary Algorithms Negative Selection
AlgorithmGenetic Algorithm Artificial Immune Recognition
SystemGenetic Programming Immune Network AlgorithmEvolution
StrategiesDendritic Cell AlgorithmDifferential
EvolutionEvolutionary ProgrammingNeural AlgorithmsGrammatical
Evolution PerceptronGene Expression Programming
Back-propagationLearning Classifier SystemHopfield
NetworkNon-dominated Sorting Genetic Algorithm Learning Vector
QuantizationStrength Pareto Evolutionary AlgorithmSelf-Organizing
Map Source: cleveralgorithms.com 27. Initial results:Does well if
giventhe parameter values. 28. Next Step:Embed a differential
genetic algorithm to evolve parameter values. 29. This is an open
question,I want your ideas! 30. I want to collaborate with you! 31.
Special Thanks to:Dr. Gregg HartvigsenThe Distributed Systems Lab
& Prof. Homma FarianThe open source community! 32.
Questions?
