Course detail

Evolutionary Computation

FIT-EVDAcad. year: 2017/2018

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

There are no prerequisites

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Study evaluation is based on marks obtained for specified items. Minimimum number of marks to pass is 50.

Course curriculum

    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.

    Syllabus - others, projects and individual work of students:
    • Defence of a project, software project based on a variant of evolutionary algorithm

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

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.

Recommended reading

Fogel D., B.: Evolutionary computation: Toward a new philosophy of machine intelligence. IEEE Press, New York, 2000, ISBN 0-7803-5379-X.

Classification of course in study plans

  • Programme CSE-PHD-4 Doctoral

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

Type of course unit

 

Lecture

39 hod., optionally

Teacher / Lecturer

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.