Course detail

Evolutionary Optimization of Systems

FSI-9EOSAcad. year: 2019/2020

This lessons are for doctorand-students who are studing evolutionary optimization.

Language of instruction

Czech

Number of ECTS credits

2

Guarantor

prof. Ing. Pavel Ošmera, CSc.

Department

Institute of Automation and Computer Science (ÚAI)

Learning outcomes of the course unit

The acquired knowledge will be sufficient for the basic orientation in the Theory of evolutionary algorithms.

Prerequisites

Basic mathematical knowledge is required.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

The course is taught through lectures explaining the basic principles and theory of the discipline.

Assesment methods and criteria linked to learning outcomes

The active participation and mastering the assigned task. Basic knowledge of an evolutionary optimization is required.

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

The course objective is to make students familiar with the basics of the Theory of evolutionary algorithms.

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

Solving an extra assignment can compensate absence.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Kvasnička V.,Pospíchal J.,Tiňo P.: Evolučné algoritmy. STU Bratislava, 2000
Golberg, D.E.: Genetic Algorithms in Search,OPtimization, and Machine Inteligence. Addisson Wesley,1989
Fogel, D.B.: Evolutionary Computation. IEEE Press, Piscataway,1995

Recommended reading

Ošmera P.: Genetické algoritmy a jejich aplikace. Habilitační práce na CD, Brno
Kolektiv autorů: sborníky CEC a GECCO. IEEE
Kolektiv autorů: sborníky MENDEL 95-04. Kunčík Jan,Brno

Type of course unit

 

Lecture

20 hours, optionally

Teacher / Lecturer

prof. Ing. Pavel Ošmera, CSc.

Syllabus

Introduction to evolutionary algorithms.
Biologically inspired computing.
History of genetic algorithms.
Description of genetic algorithms.
Basic principles of evolutionary algorithms.
Genetic algorithms wis diploid chromosomes.
Different types of genetic algorithms.
Parallel evolutionary algorithms.
Self-organization and adaptation of complex systems.
New methods of evolutionary algorithms.