Počet kreditů 6
Vyučováno v Winter
Rozsah výuky 2+2c
Garant předmětu
Přednášející Jiří Kubalík, Petr Pošík
Cvičící Jiří Kubalík, Petr Pošík

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.

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

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.

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.

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.

- 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/

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