FIT-EVDAcad. year: 2019/2020
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). 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.
Learning outcomes of the course unit
Skills and approaches in solution of hard optimization problems.
Recommended optional programme components
Recommended or required reading
Fogel D., B.: Evolutionary computation: Toward a new philosophy of machine intelligence. IEEE Press, New York, 2000, ISBN 0-7803-5379-X.
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.: Evolutionary algorithms. Publisher STU Bratislava, 2000, pp. 215, ISBN 80-227-1377-5.
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Language of instruction
To inform the students about up to date algorithms for solution of complex, NP complete problems.
Specification of controlled education, way of implementation and compensation for absences
Project defence, software project based on a variant of evolutionary algorithms or the presentation of the assigned task.
Type of course unit
39 hours, optionally
Teacher / Lecturer
- Evolutionary algorithms, theoretical foundation, basic distribution.
- Genetic algorithms (GA), schemata theory.
- Advanced genetic algorithms
- Repesentative combinatorial optimization problems.
- Evolution strategies.
- Genetic programming.
- Advanced estimation distribution algorithms (EDA).
- Variants of EDA algorithms, UMDA, BMDA and BOA.
- Simulated annealing.
- Methods for multicriterial and multimodal problems. Selection and population replacement.
- Techniques for fast prototyping. Structure of development systems and GA library.
- New evolutionary paradigm: immune systems, differential evolution, SOMA.
- Typical application tasks.