Czech

Not applicable.

Students will obtain special expertise and skills for solution of complex, NP complete problems. A part of skills, that students will acquire, includes: the ability to represent, solve problem for selected evolutionary technique, proper specification of control parameters for optimization process and choosing effective software tools for the implementation of solved problems.

Basic principles of programming in Java, C programming language, etc.

Not applicable.

Teaching is carried out through lectures that consist of interpretations of basic principles, methodology of given discipline, problems and their exemplary solutions.

Students will participate in four, two-hour computer laboratory practices, in which they will solve selected optimization problems. At the end of the semester, students have to hand project which is based upon addressing a theoretical or a practical problem. Knowledge of theoretical and practical principles of evolutionary algorithms is verified by mid-term exam and by the final examination.

1. Evolutionary algorithms, theoretical foundation, basic distributions (GA, EP,GP, ES).
2. Genetic algorithms (GA), structure, Schemata theory.
3. Genetic algorithms using diploids and messy-chromosomes. Specific crossing.
4. Evolutionary strategy (task parameters, control parameters).
5. Evolutionary programming, Hill climbing algorithm, Simulated annealing.
6. Genetic programming (principles, Symbolic Regression).
7. Advanced estimation distribution algorithms (EDA).
8. Variants of EDA algorithms, UMDA, BMDA and BOA. Bayesian network, network design.
9. Multimodal and multicriterial optimization techniques. Population selection.
10. Dynamic optimization problems.
11. New evolutionary paradigm: immune systems, differential evolution, SOMA.
12. Differential evolution. Particle swarm model.
13. Engineering tasks and evolutionary algorithms.

Not applicable.

The study aim for students is to obtain knowledge and skills for solution of complex optimization problems including mastering software tools for fast prototyping of evolutionary algorithms.

Not applicable.

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Kvasnička V., Pospíchal J.,Tiňo P.: Evolutionary algorithms. Publisher STU Bratislava, 2000, pp. 215, ISBN 80-227-1377-5.

Back, J.: Evolutionary algorithms, theory and practice, New York, 1996, ISBN:0-19-509971-0.