Course detail

Evolution Algorithms

FEKT-MEALAcad. year: 2012/2013

The course is oriented to knowledge on evolutionary computation, primarily on genetic algorithms, their realizations and use in optimization

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

doc. Ing. Jiří Kozumplík, CSc.

Department

Department of Biomedical Engineering (UBMI)

Learning outcomes of the course unit

General knowledge of evolutionary algorithms. Practical knowledge of genetic algorithms a their realizations.

Prerequisites

The subject knowledge on the Bachelor´s degree level is requested.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.

Assesment methods and criteria linked to learning outcomes

Requirements for completion of a course are specified by a regulation issued by the lecturer responsible for the course and updated for every year.

Course curriculum

Basic terms in evolutionary algorithms. Principles of genetic algorithms (GA), introduction to the theory of GA. Evolutionary strategy. Particle swarm, swarm intelligence. Differential evolution. Introduction to genetic programming.

Work placements

Not applicable.

Aims

Knowledge of evolutionary algorithms, their realizations and use. Detailed study of genetic algorithms.

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

Limitations of controlled teaching and its procedures are specified by a regulation issued by the lecturer responsible for the course and updated for every year.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Tvrdík, J.: Evoluční algoritmy. Skripta, Přírodovědecká fakulta Ostravské univerzity, 2004 (CS)

Recommended reading

Hynek, J.: Genetické algoritmy a genetické programování. Grada Publishing, 2008 (CS)

Classification of course in study plans

  • Programme EEKR-M Master's

    branch M-BEI , 2 year of study, winter semester, elective specialised

  • Programme EEKR-M Master's

    branch M-BEI , 2 year of study, winter semester, elective specialised

  • Programme EEKR-CZV lifelong learning

    branch EE-FLE , 1 year of study, winter semester, elective specialised

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

doc. Ing. Jiří Kozumplík, CSc.

Syllabus

Introduction to optimization
Basic terms in evolutionary algorithms
Principles of genetic algorithms (GA)
An introduction to the theory of GA
Examples of optimization with GA
Increase of effectiveness of GA
Parallel GA, variations of GA
Evolutionary strategy (ES), adaptive ES
Particle swarm
Swarm intelligence
Differential evolution
Introduction to genetic programming
Testing of evolutionary algorithms

Exercise in computer lab

13 hod., compulsory

Teacher / Lecturer

Ing. Martin Mézl, Ph.D.

Syllabus

Functions for testing of GA, reflected binary code
Functions for GA in Matlab
Realization of GA - simple application
Increase of effectiveness of GA
Testing of various variations of GA
Presentations of projects