MASINGER group Dr. Ferrante Neri Department of Mathematical Information Technology, University of...
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![Page 1: MASINGER group Dr. Ferrante Neri Department of Mathematical Information Technology, University of Jyväskylä, Finland 10th May 2010.](https://reader035.fdocuments.net/reader035/viewer/2022062619/5518d484550346991f8b5e7f/html5/thumbnails/1.jpg)
MASINGER group
Dr. Ferrante NeriDepartment of Mathematical Information Technology,
University of Jyväskylä, Finland
10th May 2010
![Page 2: MASINGER group Dr. Ferrante Neri Department of Mathematical Information Technology, University of Jyväskylä, Finland 10th May 2010.](https://reader035.fdocuments.net/reader035/viewer/2022062619/5518d484550346991f8b5e7f/html5/thumbnails/2.jpg)
MASINGER group
Memetic Algorithms, Swarm Intelligence, Networks, Genetic and Evolutionary Robotics
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02/17/10
Group Members 1/2
Dr. Ferrante Neri
Dr. Ernesto Mininno
Dr. Ville Tirronen
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02/17/10
Group Members 2/2
Ph. Lic Matthieu Weber
Mr. Giovanni Iacca
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02/17/10
Structure of the Group
• Horizontal and Non-hierarchical• Everybody is fundamental within its role
JUST LIKE A FOOTBALL TEAM !!
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02/17/10
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
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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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02/17/10
Research Topics in details 2/2
Applications:– Evolutionary Optimization in the Presence
of Uncertainties
– Large Scale and Computationally Expensive Optimization Problems
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Current Research Lines
Distributed Memetic/Evolutionary Algorithms
Compact Memetic/Evolutionary Algorithms
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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.
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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
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Graphical Convergence Representation
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Compact Algorithms in Real-World
AutomotiveAerospaceMedical engineeringRoboticsManufacturing
Performances depend on the control system tuning.
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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
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Questions?