Course details

Applied Evolutionary Algorithms

EVO Acad. year 2012/2013 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). Tools for fast prototyping. Representation of problems by graph models. Evolutionary algorithms in engineering applications namely in synthesis and physical design of digital circuits, artificial intelligence, signal processing, scheduling in multiprocessor systems and in business commercial applications.


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




Petrlík Jiří, 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 methodology of fast prototyping of evolutionary optimizer utilizing GA library and present design tools.

Learning objectives

Survey about actual optimization techniques and evolutionary algorithms for solution of complex, NP complete problems. To make students familiar with software tools for fast prototyping of evolutionary algorithms and learn how to solve typical complex tasks from engineering practice.

Study literature

  • Kvasnička V., Pospíchal J.,Tiňo P.: Evolutionary algorithms. Publisher STU Bratislava, 2000, pp. 215, ISBN 80-227-1377-5.
  • Kvasnička V., a kol.: Introduction into theory of neural networks, Iris 1997, ISBN 80-88778-30-1.

Fundamental literature

  • Kvasnička V., Pospíchal J.,Tiňo P.: Evolutionary algorithms. Publisher STU Bratislava, 2000, pp. 215, ISBN 80-227-1377-5.
  • Dasgupta D., Michalewicz Z.: Evolutionary algorithms in engineering applications. Springer Verlag, Berlin, 1997, ISBN 3-540-62021-4.
  • Back, J: Evolutionary algorithms, theory and practice, New York, 1996.

Syllabus of lectures

  • Evolutionary algorithms, theoretical foundation, basic distribution (GA, EP,GP, ES).
  • Genetic algorithms (GA), schemata theory.
  • Genetic algorithms using diploids and messy-chromozomes. Specific crossing.
  • Repesentative combinatorial optimization problems.
  • Evolutionary programming, Hill cimbing algorithm, Simulated annealing. 
  • Genetic programming.
  • Advanced estimation distribution algorithms (EDA).
  • Variants of EDA algorithms, UMDA, BMDA and BOA.
  • Multimodal and multicriterial optimization.
  • Dynamoc optimization problems.
  • New evolutionary paradigm: immune systems,  differential evolution, SOMA.
  • Differential evolution. Particle swarm model. 
  • Ingeneering tasks and evolutionary algorithms.


Syllabus of laboratory exercises

  • Simple design of an optimizer with GADesign system.
  • Utilizing of GA libraries like GAlib.
  • Genetic programming in Java.
  • Illustration of the program BMDA.


Syllabus - others, projects and individual work of students

  • Program for the optimization of given problem on the base of evolutionary computation.

Progress assessment

Midterm and final test, one project.

Course inclusion in study plans

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