Course details

Evolutionary Computation

EVD Acad. year 2023/2024 Summer semester

Evolutionary computation in the context of artificial intelligence and hard optimization problems. Single- and multi-objective optimization, dominance relation, Pareto front. Principles of genetic algorithms, evolutionary strategy, genetic programming and other evolutionary heuristics. Statistical evaluation of experiments. Advanced evolutionary algorithms based on probabilistic models. Parallel evolutionary algorithms. Multi-objective evolutionary algorithms. Rapid prototyping of evolutionary algorithms.

Doctoral state exam - topics:

  1. Problem encoding, genotype, phenotype, fitness function.
  2. Genetic algorithms, schema theory.
  3. Evolution strategies.
  4. Genetic programming and symbolic regression.
  5. Estimation distribution algorithms.
  6. Simulated annealing
  7. Multi-objective evolutionary optimization.
  8. Parallel evolutionary algorithms.
  9. Differential evolution, SOMA.
  10. Statistical analysis of experiments.


Language of instruction

Czech, English


Examination (oral)

Time span

  • 26 hrs lectures

Assessment points

  • 100 pts final exam


Learning objectives

To acquaint students with modern evolutionary algorithms developed for solving hard optimization and design problems.
Skills and approaches required for solving hard optimization problems using evolutionary algorithms.
Deeper understanding of the optimization problem and its solution in computer engineering.

Study literature

  • Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. 2nd ed. Springer, 2015, ISBN 978-3-662-44873-1.
  • Brabazon, A., O'Neill, M., McGarraghy, S.: Natural Computing Algorithms. Springer, 2015, ISBN 978-3-662-43630-1.
  • Doerr, B. Neumann F. (eds.): Theory of Evolutionary Computation. Springer, 2020, ISBN 978-3-030-29413-7

Syllabus of lectures

  1. Introduction to evolutionary computation.
  2. Genetic algorithms, schema theory.
  3. Statistical analysis of experiments.
  4. Typical optimization problems.
  5. Advanced techniques in genetic algorithms.
  6. Multi-objective evolutionary optimization.
  7. Evolution strategies.
  8. Genetic programming and symbolic regression.
  9. Variants of genetic programming.
  10. Parallel evolutionary algorithms.
  11. Estimation distribution algorithms.
  12. Differential evolution, SOMA and other relevant algorithms.
  13. Recent trends.

Progress assessment

Submission of the project on time, exam.
During the course, it is necessary to submit the project and pass the exam. Teaching is performed as lectures or controlled self-study; the missed classes need to be replaced by self-study.

Course inclusion in study plans

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