Course details
Evolutionary Computation
EVD Acad. year 2003/2004 Summer semester
Evolutionary computation in the context of artificial intelligence and optimization problems with NP complexity. Paradigm of genetic algorithms, evolutionary strategy, genetic programming and another evolutionary heuristics. Theory and practice of standard evolutionary computation. Advanced evolutionary algorithms based on graphic probabilistic models (EDA - estimation of distribution algorithms). Synergy of evolutionary computation and fuzzy logic. Parallel evolutionary algorithms. A survey of representative applications of evolutionary algorithms in multi-objection optimization problems, artificial intelligence, knowledge based systems and digital circuit design. Techniques of rapid prototyping of evolutionary algorithms.
Guarantor
Language of instruction
Completion
Time span
- 2 hrs lectures
Department
Subject specific learning outcomes and competences
Skills and approaches in solution of hard optimization problems.
Learning objectives
To inform the students about up to date algorithms for solution of complex, NP complete problems.
Study literature
- Fogel D., B.: Evolutionary computation: Toward a new philosophy of machine intelligence. IEEE Press, New York, 2000, ISBN 0-7803-5379-X.
Fundamental literature
- Back, J: Evolutionary algorithms, theory and practice, New York, 1996.
- Goldberg, D., E.: The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Boston, MA: Kluwer Academic Publishers, 2002. ISBN: 1402070985.
- Kvasnička V., Pospíchal J., Tiňo P.: Evoluční algoritmy. Vydavatelství STU Bratislava, 2000, str. 215, ISBN 80-227-1377-5.