MASINGER group Dr. Ferrante Neri Department of Mathematical Information Technology, University of...

MASINGER group

Dr. Ferrante NeriDepartment of Mathematical Information Technology,

University of Jyväskylä, Finland

10th May 2010

MASINGER group

Memetic Algorithms, Swarm Intelligence, Networks, Genetic and Evolutionary Robotics

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Group Members 1/2

Dr. Ferrante Neri

Dr. Ernesto Mininno

Dr. Ville Tirronen

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Group Members 2/2

Ph. Lic Matthieu Weber

Mr. Giovanni Iacca

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Structure of the Group

• Horizontal and Non-hierarchical• Everybody is fundamental within its role

JUST LIKE A FOOTBALL TEAM !!

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Research Topics at the first glance

Computational Intelligence Optimization

When the problem cannot be solved by means of an exact method due to the lack of differentiability or even analytic expression an alternative way must be found

Research Topics in details 1/2

Methodologies:– Memetic Computing

Encoding of culture into optimization algorithms, e.g. hybrid approaches, integration of knowledge– Differential Evolution

Specific Oprimization Algorithm for continuous problems

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Research Topics in details 2/2

Applications:– Evolutionary Optimization in the Presence

of Uncertainties

– Large Scale and Computationally Expensive Optimization Problems

Current Research Lines

Distributed Memetic/Evolutionary Algorithms

Compact Memetic/Evolutionary Algorithms

Distributed Algorithms• If a population is properly structured,

with no additional overhead, the performance might be significantly improved and thus highly dimensional problems (1000 D) can be handled.

Compact Algorithms

– belong to the class of Estimation Distribution Algorithms

– do not use a population of individuals– make use of a statistic representation of the

populationThis approach is necessary to solve complex optimization problems despite the absence of a full performance computer

Graphical Convergence Representation

Compact Algorithms in Real-World

AutomotiveAerospaceMedical engineeringRoboticsManufacturing

Performances depend on the control system tuning.

Application Example

•A cartesian robot controller for pick&place

•Compact algorithm to optimize the nonlinear controller (NN)

•The system has been optimized in orderto reject the unpredictable payload variation

•No external computer has been used

•Details on IEEE Computational IntelligenceMagazine, May 2010

Questions?