Faculty of Information Technology, BUT

Course details

Applied Evolutionary Algorithms

EVO Acad. year 2017/2018 Summer semester 5 credits

Overview of principles of stochastic search techniques: Monte Carlo methods, evolutionary algorithms. Detailed explanation of selected algorithms: Metropolis algorithm, simulated annealing, problems in statistical physics. Overview of basic principles of evolutionary algorithms (EA): evolutionary programming (EP), evolution strategies (ES), genetic algorithms (GA), genetic programming (GP), differential evolution (DE). Advanced evolutionary techniques: estimation of distribution algorithms (EDA), multiobjective optimization, parallel and distributed EA. Social computing algoritmhs: particle swarm optimization (PSO), ant colony optimization (ACO). Applications in engineering problems and artificial intelligence.


Bidlo Michal, Ing., Ph.D. (DCSY FIT BUT)

Language of instruction



Examination (written)

Time span

26 hrs lectures, 12 hrs pc labs, 14 hrs projects

Assessment points

60 exam, 18 labs, 22 projects



Bidlo Michal, Ing., Ph.D. (DCSY FIT BUT)


Šimek Václav, Ing. (DCSY FIT BUT)

Subject specific learning outcomes and competences

Ability of problem formulation for the solution on the base of evolutionary computation. Knowledge of analysis and design methods for evolutionary algorithms.

Learning objectives

Survey about actual optimization techniques and evolutionary algorithms for solution of complex, NP complete problems. To learn how to solve typical complex tasks from engineering practice using evolutionary techniques.

Study literature

  • Luke, S.: Essentials of Metaheuristics. Lulu, 2015, ISBN 978-1-300-54962-8
  • Jansen, T.: Analyzing Evolutionary Algorithms. Springer-Verlag, Berlin Heidelberg, 2013, ISBN 978-3-642-17338-7
  • Kvasnička, V., Pospíchal, J., Tiňo, P.: Evolučné algoritmy. STU Bratislava, Bratislava, 2000, ISBN 80-227-1377-5
  • Oplatková, Z., Ošmera, P., Šeda, M., Včelař, F., Zelinka, I.: Evoluční výpočetní techniky - principy a aplikace. BEN - technická literatura, Praha, 2008, ISBN 80-7300-218-3

Fundamental literature

  • Brabazon, A., O'Neill, M., McGarraghy, S.: Natural Computing Algorithms. Springer-Verlag Berlin Heidelberg, 2015, ISBN 978-3-662-43630-1
  • Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing, 2nd ed. Springer-Verlag Berlin Heidelberg, 2015, ISBN 978-3-662-44873-1
  • Jansen, T.: Analyzing Evolutionary Algorithms. Springer-Verlag, Berlin Heidelberg, 2013, ISBN 978-3-642-17338-7
  • Talbi, E.-G.: Metaheuristics: From Design to Implementation. Wiley, Hoboken, New Jersey, 2009, ISBN 978-0-470-27858-1
  • Bäck, T.: Evolutionary Algorithms in Theory and Practice. Oxford University Press, Oxford, 1996, ISBN 978-0195099713

Syllabus of lectures

  1. Introduction, terminology, principles of stochastic search algorithms.
  2. Monte Carlo method and variants (Metropolis algorithm, Simulated Annealing).
  3. Basic evolutionary algorithms (Evolutionary Programming, Evolution Strategies).
  4. Genetic algorithms (control parameters, genetic operators).
  5. Genetic programming and symbolic regression.
  6. Case studies (design of algorithms and electronic circuits).
  7. Differential evolution (numerical optimization, engineering case study).
  8. Advanced evolutionary techniques (Estimation of Distribution Algorithms).
  9. Multiobjective evolutionary algorithms (basic techniques, engineering case study).
  10. Advanced multiobjective evolutionary algorithms.
  11. Parallel evolutionary algorithms and coevolutionary algorithms.
  12. Evolutionary development and grammatical evolution.
  13. Social computing algorithms (Particle Swarm Optimization, Ant algorithms).

Syllabus - others, projects and individual work of students

Solution of a task selected from topics published for the actual academic year.

By agreement there is a possibility to accept the project from other courses (e.g. BIN) for EVO if its topic relates to evolutionary computation and the solution fulfils requirements of EVO projects.

Progress assessment

6 computer practices (at most 3 points for each), a project with ongoing work defense and final defense (in summary for at most 22 points). In case of documented study impediments an additional term will be specified to substitute the missed practice(s).

Exam prerequisites


Course inclusion in study plans

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