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, English
Number of ECTS credits
2
Mode of study
Not applicable.
Guarantor
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
Fogel, D.B.: Evolutionary Computation. IEEE Press, Piscataway,1995
Golberg, D.E.: Genetic Algorithms in Search,OPtimization, and Machine Inteligence. Addisson Wesley,1989
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
Kvasnička V.,Pospíchal J.,Tiňo P.: Evolučné algoritmy. STU Bratislava, 2000
Recommended reading
Kolektiv autorů: sborníky CEC a GECCO. IEEE
Kolektiv autorů: sborníky MENDEL 95-04. Kunčík Jan,Brno
Ošmera P.: Genetické algoritmy a jejich aplikace. Habilitační práce na CD, Brno
Kolektiv autorů: sborníky MENDEL 95-04. Kunčík Jan,Brno
Ošmera P.: Genetické algoritmy a jejich aplikace. Habilitační práce na CD, Brno
Type of course unit
Lecture
20 hod., optionally
Teacher / Lecturer
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.
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.