Course details

Evolutionary Computation

EVD Acad. year 2018/2019 Summer semester

Current academic year

Evolutionary computation in the context of artificial intelligence and optimization problems with NP complexity. Paradigm of genetic algorithms, evolutionary strategy, genetic programming and another evolutionary heuristics. Theory and practice of standard evolutionary computation. Advanced evolutionary algorithms based on graphic probabilistic models (EDA - estimation of distribution algorithms). Parallel evolutionary algorithms. A survey of representative applications of evolutionary algorithms in multi-objection optimization problems, artificial intelligence, knowledge based systems and digital circuit design. Techniques of rapid prototyping of evolutionary algorithms.

Guarantor

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

Language of instruction

Czech, English

Completion

Examination (oral)

Time span

  • 39 hrs lectures

Assessment points

  • 51 pts final exam
  • 49 pts projects

Department

Department of Computer Systems (UPSY)

Lecturer

Sekanina Lukáš, prof. Ing., Ph.D. (DCSY)
Schwarz Josef, doc. Ing., CSc.

Instructor

Sekanina Lukáš, prof. Ing., Ph.D. (DCSY)
Schwarz Josef, doc. Ing., CSc.

Subject specific learning outcomes and competences

Skills and approaches in solution of hard optimization problems.

Learning objectives

To inform the students about up to date algorithms for solution of complex, NP complete problems.

Study literature

  • Fogel D., B.: Evolutionary computation: Toward a new philosophy of machine intelligence. IEEE Press, New York, 2000, ISBN 0-7803-5379-X.
  • Back, J: Evolutionary algorithms, theory and practice, New York, 1996.
  • Goldberg, D., E.: The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Boston, MA: Kluwer Academic Publishers, 2002. ISBN: 1402070985.
  • Kvasnička V., Pospíchal J., Tiňo P.: Evolutionary algorithms. Publisher STU Bratislava, 2000, pp. 215, ISBN 80-227-1377-5.

Syllabus of lectures

  • Evolutionary algorithms, theoretical foundation, basic distribution.
  • Genetic algorithms (GA), schemata theory.
  • Advanced genetic algorithms
  • Repesentative combinatorial optimization problems.
  • Evolution strategies.
  • Genetic programming.
  • Advanced estimation distribution algorithms (EDA).
  • Variants of EDA algorithms, UMDA, BMDA and BOA.
  • Simulated annealing.
  • Methods for multicriterial and multimodal problems. Selection and population replacement.
  • Techniques for fast prototyping. Structure of development systems and GA library.
  • New evolutionary paradigm: immune systems,  differential evolution, SOMA.
  • Typical application tasks.

Controlled instruction

Project defence, software project based on a variant of evolutionary algorithms or the  presentation of the assigned task.

Course inclusion in study plans

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