Genetic algorithms

A tool for modelling, simulation, and optimization of complex systems

verfasst von: Helena Szczerbicka, Matthias Becker, Michael Syrjakow
Abstract: Until very recently genetic algorithms GAs were considered to be the proprietary field of general systems theoreticians and important for esoteric or extremely complex optimization studies. This paper endeavors to show that GA are of great utility in cases where complex systems have to be designed and, therefore, rational choices have to be made. The GA approach is based loosely on the theory of natural evolution, genetic diversity, and searching for beneficial adaptations to a complicated and changing environment. GAs can be viewed as a modelling tool and as a technique for simulation of complex systems represented by communities of interacting units. The representation of units can express characteristics, capabilities, or relatively simple strategies. These units compete and are modified by external operators, so that the overall system adapts to its environment. That environment defines the criterion by which the success in adapting can be measured. Genetic algorithms have been successfully applied to many optimization problems including mathematical function optimization, very large scale integration VLSI chip layout, molecular docking, parameter fitting, scheduling, manufacturing, clustering, machine learning, etc. and are still finding increasing acceptance. Modelling and optimization of a Kanban system from the field of flexible manufacturing systems is discussed in the last section.
Externe Organisation(en): Universität Bremen
Karlsruher Institut für Technologie (KIT)
Typ: Artikel
Journal: Cybernetics and Systems
Band: 29
Seiten: 639-659
Anzahl der Seiten: 21
ISSN: 0196-9722
Publikationsdatum: 29.10.1998
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Software, Information systems, Artificial intelligence
Elektronische Version(en): https://doi.org/10.1080/019697298125461 (Zugang: Geschlossen)

BibTeX

@article{c4134d0d852348a49b0c5a963c835547,
title = "Genetic algorithms: A tool for modelling, simulation, and optimization of complex systems",
abstract = "Until very recently genetic algorithms GAs were considered to be the proprietary field of general systems theoreticians and important for esoteric or extremely complex optimization studies. This paper endeavors to show that GA are of great utility in cases where complex systems have to be designed and, therefore, rational choices have to be made. The GA approach is based loosely on the theory of natural evolution, genetic diversity, and searching for beneficial adaptations to a complicated and changing environment. GAs can be viewed as a modelling tool and as a technique for simulation of complex systems represented by communities of interacting units. The representation of units can express characteristics, capabilities, or relatively simple strategies. These units compete and are modified by external operators, so that the overall system adapts to its environment. That environment defines the criterion by which the success in adapting can be measured. Genetic algorithms have been successfully applied to many optimization problems including mathematical function optimization, very large scale integration VLSI chip layout, molecular docking, parameter fitting, scheduling, manufacturing, clustering, machine learning, etc. and are still finding increasing acceptance. Modelling and optimization of a Kanban system from the field of flexible manufacturing systems is discussed in the last section.",
author = "Helena Szczerbicka and Matthias Becker and Michael Syrjakow",
year = "1998",
month = oct,
day = "29",
doi = "10.1080/019697298125461",
language = "English",
volume = "29",
pages = "639--659",
journal = "Cybernetics and Systems",
issn = "0196-9722",
publisher = "Taylor and Francis Ltd.",
number = "7",
}