Faculty of Information Technology, BUT

Course details

Applied Evolutionary Algorithms

EVO Acad. year 2016/2017 Summer semester 5 credits

Current academic year

Multiobjective optimization problems, standard approaches and stochastic evolutionary algorithms (EA), simulated annealing (SA). Evolution strategies (ES) and genetic algorithms (GA). Representation of problems by graph models. Evolutionary algorithms in engineering applications namely in synthesis and physical design of digital circuits and artificial intelligence.


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

Language of instruction



Examination (written)

Time span

26 hrs lectures, 8 hrs pc labs, 18 hrs projects

Assessment points

51 exam, 18 half-term test, 8 labs, 23 projects



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


Hyrš Martin, 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, principles of stochastic search algorithms.
  2. Basic evolutionary algorithms (evolutionary programming, evolution strategies).
  3. Genetic algorithms (principles, parameters, genetic operators).
  4. Genetic programming (principles, symbolic regression)
  5. Case studies: desihn of sorting networks, evolution of cellular automata.
  6. Numerical optimization, differential evolution.
  7. Social computing algorithms (Particle Swarm Optimization, Ant Colony Algorithms).
  8. Advanced estimation distribution algorithms.
  9. Evolutionary development, grammatical evolution.
  10. Multiobjective evolutionary algorithms.
  11. Parallel evolutionary algorithms.
  12. Coevolutionary algorithms.
  13. Other selected nature-inspired paradigmas.

Syllabus of laboratory exercises

  • Basic concepts of evolutionary computing, typical problems, solution of a technical task using a variant of Metropolis algorithm.
  • Evolutionary algorithms in engineering areas, optimization of electronic circuits using genetic algorithm.
  • Evolutionary design using genetic programming.
  • Advanced nature-inspired algorithms.

Syllabus - others, projects and individual work of students

  • Implementation of a given application from the field of evolutionary computation or
  • study of a given paper, presentation of main ideas.
By agreement there is a possibility to include solution of the project from other course (e.g. BIN) to EVO if its topic belongs to evolutionary computation.

Progress assessment

Midterm and final test, one project.

Course inclusion in study plans

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