Table of Contents

Lectures

The schedule for winter semester 2025/26 is shown below. The linked lecture materials come from previous runs of the course, and they can (and probably will) be updated during the actual semester. The order of lectures can change.

Lecturer:

Competencies you should get after each lecture.

Schedule

The order and content of the lectures can change, including the competencies!

date week Contents Materials Reading
22.09. Not recorded 1. Optimization. Local search and evolutionary algorithms. Slides. Handouts. (Updated 20220919.) Chapters 1, 2, intro of 3 and 3.2 from [Luke2009]
Lecture in the exercise time slot: Successful applications of EAs. applications_of_eas_2022.pptx (WARNING: BIG FILE), applications_of_eas_2022.pdf
29.09. BBB rec. 2. Discrete EAa. Binary representation, permutations. Slides. Handouts. (Updated 20210929.) MTSP video Chapters 3.2, 3.3, 4.1 from [Luke2009].
06.10. BBB rec. 3. EAs with real representation. Slides. Handouts. (Updated 20221003.) Chapters 3.1, 3.2, 3.4, 4.1, 9.2.3 from [Luke2009].
13.10. BBB rec. 4. EA with real representation (part 2). Other types of metaheuristics: PSO. ACO. Slides. Handouts. (Updated 20221010.) Chapters 3.4, 3.5 and 8 (esp. 8.3) from [Luke2009].
20.10. BBB rec. 5. No Free Lunch. Comparing performance of EAs. Slides. Handouts. (Updated 20221017.) Chapter 11 from [Luke2009], esp. 11.1 - 11.2.
27.10. 6. Constraints. Penalization, stochastic ranking, multiobjective approach. Slides. Handouts. (Updated 20231113.) /
03.11. 7. Multiobjective optimization. Dominance, Pareto optimality. NSGAII, SPEA2, MOEA/D. Slides. Handouts. (Updated 20231119.) Examples of real applications MOEA/D tutorial Chapter 7 from [Luke2009].
10.11. 8. Genetic programming. Basic principles and applications. Slides. Handouts. (Updated 20231127.) Chapters 3.3.3, 4.3 from [Luke2009].
17.11. 9. Holiday
24.11. 10. Grammatical evolution, Cartesian GP. Slides. Handouts. (Updated 20211115.)
01.12. 11. Estimation of Distribution Algorithms. Slides. Handouts. (Updated 20211122.) Chapter 9 from [Luke2009].
08.12. 12. Parallel EAs, coevolution. Slides. Handouts. (Updated 20211129) Chapters 5 and 6 from [Luke2009].
15.12. 13. Parameters of EAs: tuning and adaptation. Slides. Handouts. (Updated 20211206.)
05.01. 14. Presentations of semestral tasks.

Bonus for the interested ones:

Quality-Diversity Optimization (watch online) GECCO 2021 QD Tutorial + slides (access from FEL or via FEL VPN). Alternative: ICML 2019 Tutorial

Recordings

2025/26

Since a substantial part of students cannot be present at lectures due to schedule conflict, I decided to start recording the lectures (from the 2nd lecture onwards).

  1. Intro to optimization + Applications of EAs. Not recorded.
  2. Simple genetic algorithms. BBB
  3. Simple GAs (completion). Evolution Strategies (start). BBB.
  4. Evolution Strategies + other types of metaheuristics. BBB.
  5. No Free Lunch + Empirical Comparisons of Stochastic Algorithms. BBB.

2020/21

Below you can find recordings of online lectures in BigBlueButton from 2020/21 with Jiří Kubalík and Petr Pošík as the lecturers. They are now located in archive, and it make take several minutes before the recording loads.

  1. Intro to optimization + Applications of EAs. BBB. (Bad sound in the first part. :-( )
  2. Simple genetic algorithms. BBB.
  3. Evolution Strategies. BBB.
  4. Evolution Strategies + other types of metaheuristics. BBB.
  5. No Free Lunch + Empirical Comparisons of Stochastic Algorithms. BBB.
  6. Multiobjective EAs. BBB.
  7. Constraints. BBB.
  8. Genetic Programming. BBB.
  9. Grammatical Evolution. Cartesian GP. BBB.
  10. Estimation of Distribution Algorithms. BBB.
  11. Parallel Genetic Algorithms. Coevolution. BBB.
  12. GP Issues. Parameter Tuning. BBB.