Course detail

Evolutionary Computation

FIT-EVDAcad. year: 2025/2026

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. Parallel evolutionary algorithms. Multi-objective evolutionary algorithms. Evolutionary machine learning.


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. Simulated annealing
  6. Multi-objective evolutionary optimization.
  7. Parallel evolutionary algorithms.
  8. Similar search algorithms, e.g., differential evolution, swarm algorithms.
  9. Statistical analysis of experiments.
  10. Evolutionary machine learning.

Language of instruction

Czech, English

Mode of study

Not applicable.

Guarantor

prof. Ing. Lukáš Sekanina, Ph.D.

Department

Department of Computer Systems (UPSY)

Entry knowledge

Not applicable.

Rules for evaluation and completion of the course

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.

Aims

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.
A deeper understanding of the optimization problem and its possible solutions in computer engineering.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Not applicable.

Recommended reading

Banzhaf, W., Machado, P., Zhang, M. (eds): Handbook of Evolutionary Machine Learning. Springer, 2023, ISBN 978-981-99-3813-1.
Brabazon, A., O'Neill, M., McGarraghy, S.: Natural Computing Algorithms. Springer, 2015, ISBN 978-3-662-43630-1.
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. 2nd ed. Springer, 2015, ISBN 978-3-662-44873-1.

Classification of course in study plans

  • Programme DIT Doctoral 0 year of study, summer semester, compulsory-optional
  • Programme DIT Doctoral 0 year of study, summer semester, compulsory-optional
  • Programme DIT-EN Doctoral 0 year of study, summer semester, compulsory-optional
  • Programme DIT-EN Doctoral 0 year of study, summer semester, compulsory-optional

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

prof. Ing. Lukáš Sekanina, Ph.D.

Syllabus

  1. Introduction to evolutionary computation.
  2. Genetic algorithms, schema theory.
  3. Typical optimization problems.
  4. Statistical analysis of experiments.
  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. Similar algorithms (differential evolution, swarm algorithms).
  12. Evolutionary machine learning.
  13. Recent trends.

Guided consultation in combined form of studies

26 hod., optionally

Teacher / Lecturer

prof. Ing. Lukáš Sekanina, Ph.D.