A dynamically switched crossover for genetic algorithms

Liang Ming; Yiu Ming Cheung; Yu Ping Wang

A dynamically switched crossover for genetic algorithms

Liang Ming^*, Yiu Ming Cheung, Yu Ping Wang

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

3 Citations (Scopus)

Abstract

The traditional crossover operator performs the constant crossover between two parents without considering their homogeneity. Actually, the more homogeneous the parents are, the more disruptive the crossover should be. In this paper, a self-adaptive mechanism named Adaptive Recombination with Three Sub-populations (ARTS) is therefore presented to control the crossover operator of a genetic algorithm. The ARTS allows the crossover to be dynamically switched among two-point crossover (i.e., the least disruptive crossover), uniform crossover with probability 0.2, and uniform crossover with probability 0.5 (i.e., the most disruptive crossover). The experiments have shown the promising results.

Original language	English
Title of host publication	Proceedings of 2004 International Conference on Machine Learning and Cybernetics
Pages	3254-3257
Number of pages	4
Publication status	Published - 2004
Event	Proceedings of 2004 International Conference on Machine Learning and Cybernetics - Shanghai, China Duration: 26 Aug 2004 → 29 Aug 2004

Publication series

Name	Proceedings of 2004 International Conference on Machine Learning and Cybernetics
Volume	5

Conference

Conference	Proceedings of 2004 International Conference on Machine Learning and Cybernetics
Country/Territory	China
City	Shanghai
Period	26/08/04 → 29/08/04

Scopus Subject Areas

Engineering(all)

User-Defined Keywords

Adaptive recombination with three sub-populations (ARTS)
Crossover operator
Dynamic switch
Genetic algorithm

Cite this

@inproceedings{d8c5c8ec356e4878a83e0fc844050769,

title = "A dynamically switched crossover for genetic algorithms",

abstract = "The traditional crossover operator performs the constant crossover between two parents without considering their homogeneity. Actually, the more homogeneous the parents are, the more disruptive the crossover should be. In this paper, a self-adaptive mechanism named Adaptive Recombination with Three Sub-populations (ARTS) is therefore presented to control the crossover operator of a genetic algorithm. The ARTS allows the crossover to be dynamically switched among two-point crossover (i.e., the least disruptive crossover), uniform crossover with probability 0.2, and uniform crossover with probability 0.5 (i.e., the most disruptive crossover). The experiments have shown the promising results.",

keywords = "Adaptive recombination with three sub-populations (ARTS), Crossover operator, Dynamic switch, Genetic algorithm",

author = "Liang Ming and Cheung, {Yiu Ming} and Wang, {Yu Ping}",

note = "Copyright: Copyright 2008 Elsevier B.V., All rights reserved.; Proceedings of 2004 International Conference on Machine Learning and Cybernetics ; Conference date: 26-08-2004 Through 29-08-2004",

year = "2004",

language = "English",

isbn = "0780384032",

series = "Proceedings of 2004 International Conference on Machine Learning and Cybernetics",

pages = "3254--3257",

booktitle = "Proceedings of 2004 International Conference on Machine Learning and Cybernetics",

}

Ming, L, Cheung, YM & Wang, YP 2004, A dynamically switched crossover for genetic algorithms. in Proceedings of 2004 International Conference on Machine Learning and Cybernetics. Proceedings of 2004 International Conference on Machine Learning and Cybernetics, vol. 5, pp. 3254-3257, Proceedings of 2004 International Conference on Machine Learning and Cybernetics, Shanghai, China, 26/08/04.

A dynamically switched crossover for genetic algorithms. / Ming, Liang; Cheung, Yiu Ming; Wang, Yu Ping.
Proceedings of 2004 International Conference on Machine Learning and Cybernetics. 2004. p. 3254-3257 (Proceedings of 2004 International Conference on Machine Learning and Cybernetics; Vol. 5).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - A dynamically switched crossover for genetic algorithms

AU - Ming, Liang

AU - Cheung, Yiu Ming

AU - Wang, Yu Ping

PY - 2004

Y1 - 2004

N2 - The traditional crossover operator performs the constant crossover between two parents without considering their homogeneity. Actually, the more homogeneous the parents are, the more disruptive the crossover should be. In this paper, a self-adaptive mechanism named Adaptive Recombination with Three Sub-populations (ARTS) is therefore presented to control the crossover operator of a genetic algorithm. The ARTS allows the crossover to be dynamically switched among two-point crossover (i.e., the least disruptive crossover), uniform crossover with probability 0.2, and uniform crossover with probability 0.5 (i.e., the most disruptive crossover). The experiments have shown the promising results.

AB - The traditional crossover operator performs the constant crossover between two parents without considering their homogeneity. Actually, the more homogeneous the parents are, the more disruptive the crossover should be. In this paper, a self-adaptive mechanism named Adaptive Recombination with Three Sub-populations (ARTS) is therefore presented to control the crossover operator of a genetic algorithm. The ARTS allows the crossover to be dynamically switched among two-point crossover (i.e., the least disruptive crossover), uniform crossover with probability 0.2, and uniform crossover with probability 0.5 (i.e., the most disruptive crossover). The experiments have shown the promising results.

KW - Adaptive recombination with three sub-populations (ARTS)

KW - Crossover operator

KW - Dynamic switch

KW - Genetic algorithm

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AN - SCOPUS:6344285790

SN - 0780384032

T3 - Proceedings of 2004 International Conference on Machine Learning and Cybernetics

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BT - Proceedings of 2004 International Conference on Machine Learning and Cybernetics

T2 - Proceedings of 2004 International Conference on Machine Learning and Cybernetics

Y2 - 26 August 2004 through 29 August 2004

ER -

A dynamically switched crossover for genetic algorithms

Abstract

Publication series

Conference

Scopus Subject Areas

User-Defined Keywords

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