Adaptive Crossover and Mutation in Genetic Algorithms Based on Clustering Technique

Jun ZHANG; Henry, S.H. Chung; Jinghui Zhong

doi:10.1145/1068009.1068267

Adaptive Crossover and Mutation in Genetic Algorithms Based on Clustering Technique

Jun ZHANG, Henry, S.H. Chung, Jinghui Zhong

Department of Electrical Engineering

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

12 Citations (Scopus)

Abstract

Instead of having fixed p_x and p_m, this paper presents the use of fuzzy logic to adaptively tune p_x and p_m for optimization of power electronic circuits throughout the process. By applying the K-means algorithm, distribution of the population in the search space is clustered in each training generation. Inferences of p_x and p_m are performed by a fuzzy-based system that fuzzifies the relative sizes of the clusters containing the best and worst chromosomes. The proposed adaptation method is applied to optimize a buck regulator that requires satisfying some static and dynamic requirements. The optimized circuit component values, the regulator's performance, and the convergence rate in the training are favorably compared with the GA's using fixed p_x and p_m.

Original language	English
Title of host publication	GECCO 2005 - Genetic and Evolutionary Computation COnference
Editors	Hans-Georg Beyer
Publisher	Association for Computing Machinery
Pages	1577-1578
Volume	2
ISBN (Print)	1-59593-010-8
DOIs	https://doi.org/10.1145/1068009.1068267
Publication status	Published - Jun 2005
Event	7th Annual Genetic and Evolutionary Computation COnference (GECCO-2005) - Washington, United States Duration: 25 Jun 2005 → 29 Jun 2005 http://www.isgec.org/gecco-2005/

Conference

Conference	7th Annual Genetic and Evolutionary Computation COnference (GECCO-2005)
Place	United States
City	Washington
Period	25/06/05 → 29/06/05
Internet address	http://www.isgec.org/gecco-2005/

Research Keywords

Genetic Algorithms
Real World Applications

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10.1145/1068009.1068267

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title = "Adaptive Crossover and Mutation in Genetic Algorithms Based on Clustering Technique",

abstract = "Instead of having fixed px and pm, this paper presents the use of fuzzy logic to adaptively tune px and pm for optimization of power electronic circuits throughout the process. By applying the K-means algorithm, distribution of the population in the search space is clustered in each training generation. Inferences of px and pm are performed by a fuzzy-based system that fuzzifies the relative sizes of the clusters containing the best and worst chromosomes. The proposed adaptation method is applied to optimize a buck regulator that requires satisfying some static and dynamic requirements. The optimized circuit component values, the regulator's performance, and the convergence rate in the training are favorably compared with the GA's using fixed px and pm.",

keywords = "Genetic Algorithms, Real World Applications",

author = "Jun ZHANG and Chung, {Henry, S.H.} and Jinghui Zhong",

year = "2005",

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booktitle = "GECCO 2005 - Genetic and Evolutionary Computation COnference",

publisher = "Association for Computing Machinery",

address = "United States",

note = "7th Annual Genetic and Evolutionary Computation COnference (GECCO-2005) ; Conference date: 25-06-2005 Through 29-06-2005",

url = "http://www.isgec.org/gecco-2005/",

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ZHANG, J, Chung, HSH & Zhong, J 2005, Adaptive Crossover and Mutation in Genetic Algorithms Based on Clustering Technique. in H-G Beyer (ed.), GECCO 2005 - Genetic and Evolutionary Computation COnference. vol. 2, Association for Computing Machinery, pp. 1577-1578, 7th Annual Genetic and Evolutionary Computation COnference (GECCO-2005), Washington, District of Columbia, United States, 25/06/05. https://doi.org/10.1145/1068009.1068267

Adaptive Crossover and Mutation in Genetic Algorithms Based on Clustering Technique. / ZHANG, Jun; Chung, Henry, S.H.; Zhong, Jinghui.
GECCO 2005 - Genetic and Evolutionary Computation COnference. ed. / Hans-Georg Beyer. Vol. 2 Association for Computing Machinery, 2005. p. 1577-1578.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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AU - Chung, Henry, S.H.

AU - Zhong, Jinghui

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N2 - Instead of having fixed px and pm, this paper presents the use of fuzzy logic to adaptively tune px and pm for optimization of power electronic circuits throughout the process. By applying the K-means algorithm, distribution of the population in the search space is clustered in each training generation. Inferences of px and pm are performed by a fuzzy-based system that fuzzifies the relative sizes of the clusters containing the best and worst chromosomes. The proposed adaptation method is applied to optimize a buck regulator that requires satisfying some static and dynamic requirements. The optimized circuit component values, the regulator's performance, and the convergence rate in the training are favorably compared with the GA's using fixed px and pm.

AB - Instead of having fixed px and pm, this paper presents the use of fuzzy logic to adaptively tune px and pm for optimization of power electronic circuits throughout the process. By applying the K-means algorithm, distribution of the population in the search space is clustered in each training generation. Inferences of px and pm are performed by a fuzzy-based system that fuzzifies the relative sizes of the clusters containing the best and worst chromosomes. The proposed adaptation method is applied to optimize a buck regulator that requires satisfying some static and dynamic requirements. The optimized circuit component values, the regulator's performance, and the convergence rate in the training are favorably compared with the GA's using fixed px and pm.

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KW - Real World Applications

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DO - 10.1145/1068009.1068267

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EP - 1578

BT - GECCO 2005 - Genetic and Evolutionary Computation COnference

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ER -

Adaptive Crossover and Mutation in Genetic Algorithms Based on Clustering Technique

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