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.

Language of instruction

Czech

Mode of study

Not applicable.

Guarantor

prof. Ing. Lukáš Sekanina, Ph.D.

Department

Department of Computer Systems (UPSY)

Learning outcomes of the course unit

Skills and approaches in solution of hard optimization problems.

Prerequisites

Not applicable.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Not applicable.

Course curriculum

Not applicable.

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.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Not applicable.

Recommended reading

Back, J: Evolutionary algorithms, theory and practice, New York, 1996.
Fogel D., B.: Evolutionary computation: Toward a new philosophy of machine intelligence. IEEE Press, New York, 2000, ISBN 0-7803-5379-X.
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.

Classification of course in study plans

  • Programme CSE-PHD-4 Doctoral

    branch DVI4 , 0 year of study, summer semester, elective

  • Programme CSE-PHD-4 Doctoral

    branch DVI4 , 0 year of study, summer semester, elective

Type of course unit

 

Lecture

39 hod., optionally

Teacher / Lecturer

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

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.

Guided consultation in combined form of studies

26 hod., optionally

Teacher / Lecturer

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