Details
| Original language | English |
|---|---|
| Pages (from-to) | 639-659 |
| Number of pages | 21 |
| Journal | Cybernetics and Systems |
| Volume | 29 |
| Issue number | 7 |
| Publication status | Published - 29 Oct 1998 |
| Externally published | Yes |
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.
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Information Systems
- Computer Science(all)
- Artificial Intelligence
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In: Cybernetics and Systems, Vol. 29, No. 7, 29.10.1998, p. 639-659.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Genetic algorithms
T2 - A tool for modelling, simulation, and optimization of complex systems
AU - Szczerbicka, Helena
AU - Becker, Matthias
AU - Syrjakow, Michael
PY - 1998/10/29
Y1 - 1998/10/29
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0032186746&partnerID=8YFLogxK
U2 - 10.1080/019697298125461
DO - 10.1080/019697298125461
M3 - Article
AN - SCOPUS:0032186746
VL - 29
SP - 639
EP - 659
JO - Cybernetics and Systems
JF - Cybernetics and Systems
SN - 0196-9722
IS - 7
ER -