Course detail

Evolutionary Computation

FIT-EVDAcad. year: 2019/2020

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.

Learning outcomes of the course unit

Skills and approaches in solution of hard optimization problems.

Prerequisites

Not applicable.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

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.: Evoluční algoritmy. Vydavatelství STU Bratislava, 2000, str. 215, ISBN 80-227-1377-5.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Not applicable.

Language of instruction

Czech, English

Work placements

Not applicable.

Aims

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

Specification of controlled education, way of implementation and compensation for absences

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

Classification of course in study plans

  • Programme VTI-DR-4 Doctoral

    branch DVI4 , any year of study, summer semester, 0 credits, optional

  • Programme VTI-DR-4 Doctoral

    branch DVI4 , any year of study, summer semester, 0 credits, optional

  • Programme VTI-DR-4 Doctoral

    branch DVI4 , any year of study, summer semester, 0 credits, optional

  • Programme VTI-DR-4 Doctoral

    branch DVI4 , any year of study, summer semester, 0 credits, optional

Type of course unit

 

Lecture

39 hours, optionally

Teacher / Lecturer

Syllabus


  • 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.

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