Subject description - A0M33EOA

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A0M33EOA Evolutionary Optimization Algorithms Extent of teaching:2P+2C
Guarantors:Pošík P. Roles:V Language of
teaching:
CS
Teachers:Kubalík J., Pošík P. Completion:Z,ZK
Responsible Department:13133 Credits:6 Semester:Z

Anotation:

The course aims at issues related to the application of evolutionary algorithms in practice and at the methods used to solve them. Evolutionary algorithms are optimization metaheuristics that use analogies with natural evolution to solve complex optimization tasks. The course builds on and extends knowledge from the course Bio-inspired algorithms. In the seminar and lab lectures, the students will get hands-on tutorials and will be obliged to implement their own evolutionary algorithm to solve an optimization task as part of their project.

Study targets:

The main goal of this course is to introduce several forms of evolutionary optimization algorithms in detail along with suitable application areas. The emphasis is given to problems encountered when applying the evolutionary algorithms, and on the methods usable to overcome them.

Course outlines:

1. Standard evolutionary algorithms (EAs). A relation of EAs to the classical optimization techniques.
2. No-Free-Lunch theorem. Evaluation EAs performance.
3. Working with constraints -- special representation, penalization, decoders and repairing algorithms, multiobjective approach.
4. EA's control parameters -- tuning and adaptation.
5. Statistical dependence of solution components. Perturbation methods.
6. Estimation of distribution algorithms (EDA).
7. Evolutionary strategy with covariance matrix adaptation.
8. Parallel EAs.
9. Genetic programming (GP) -- representation, initialization, genetic operators, typed GP, automatically defined functions.
10. Grammatical evolution, gene expression programming.
11. Linear genetic programming, graph-based genetic programming.
12. GP issues -- 'bloat', diversity preservation.
13. Coevolution.
14.

Exercises outline:

1. Implementation of simple genetic algorithm (SGA). Influence of individual parameter values.
2. Analysis of the topics for the seminar project.
3. Seminar project elaboration. Part I - local optimization algorithm.
4. Seminar project elaboration. Part I - local optimization algorithm.
5. Hand-in of the seminar project I.
6. Seminar project elaboration. Part II - a simple EA vs. specialized EA or memetic algorithm.
7. Seminar project elaboration. Part II - a simple EA vs. specialized EA or memetic algorithm.
8. Seminar project elaboration. Part II - a simple EA vs. specialized EA or memetic algorithm.
9. Successful applications of EAs.
10. Seminar project elaboration. Part II - a simple EA vs. specialized EA or memetic algorithm.
11. Hand-in of the seminar project and presentations of the results.
12. Test.
13. Hand-in of the seminar project and presentations of the results.
14.

Literature:

- Luke, S.: Essentials of Metaheuristics, 2009 http://cs.gmu.edu/~sean/book/metaheuristics/ - Poli, R., Langdon, W., McPhee, N.F.: A Field Guide to Genetic Programming, 2008 http://www.gp-field-guide.org.uk/

Requirements:

Basic understanding of optimization and optimization methods. Course info: https://cw.felk.cvut.cz/doku.php/courses/a0m33eoa/start

Webpage:

http://cw.felk.cvut.cz/doku.php/courses/a0m33eoa/start

Keywords:

Evolutionary algorithms, genetic programming, evolution strategies, optimization.

Subject is included into these academic programs:

Program Branch Role Recommended semester
MPBIO1 Biomedical Informatics V
MPBIO2 Biomedical Engineering V
MPIB Common courses V
MPKME1 Wireless Communication V
MPKME5 Systems of Communication V
MPKME4 Networks of Electronic Communication V
MPKME3 Electronics V
MPKME2 Multimedia Technology V
MPEEM1 Technological Systems V
MPEEM5 Economy and Management of Electrical Engineering V
MPEEM4 Economy and Management of Power Engineering V
MPEEM3 Electrical Power Engineering V
MPEEM2 Electrical Machines, Apparatus and Drives V
MPKYR4 Aerospace Systems V
MPKYR1 Robotics V
MPKYR3 Systems and Control V
MPKYR2 Sensors and Instrumentation V
MPOI1 Artificial Intelligence V
MPOI5NEW Software Engineering V
MPOI4NEW Computer Graphics and Interaction V
MPOI5 Software Engineering V
MPOI4 Computer Graphics and Interaction V
MPOI3 Computer Vision and Image Processing V
MPOI2 Computer Engineering V


Page updated 12.12.2019 15:52:11, semester: Z,L/2020-1, L/2018-9, Z,L/2019-20, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)