Course details

Evolutionary Computation

EVD Acad. year 2024/2025 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. 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 algorithms, e.g., differential evolution, swarm algorithms.
  9. Statistical analysis of experiments.
  10. Evolutionary machine learning.

Guarantor

Sekanina Lukáš, prof. Ing., Ph.D. (DCSY)

Language of instruction

Czech, English

Completion

Examination (oral)

Time span

  • 26 hrs lectures

Assessment points

  • 100 pts final exam

Department

Department of Computer Systems (UPSY)

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.
A 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.
  • Banzhaf, W., Machado, P., Zhang, M. (eds): Handbook of Evolutionary Machine Learning. Springer, 2023, ISBN 978-981-99-3813-1.

Syllabus of lectures

  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.

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

  • Programme DIT, any year of study, Compulsory-Elective group O
  • Programme DIT, any year of study, Compulsory-Elective group O
  • Programme DIT-EN (in English), any year of study, Compulsory-Elective group O
  • Programme DIT-EN (in English), any year of study, Compulsory-Elective group O
  • Programme VTI-DR-4, field DVI4, any year of study, Elective
  • Programme VTI-DR-4, field DVI4, any year of study, Elective
  • Programme VTI-DR-4 (in English), field DVI4, any year of study, Elective
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