Adaptive genetic algorithm based on density distribution of population

Ni Chen; Jun Zhang; Ou Liu

doi:10.1145/2330784.2331039

Adaptive genetic algorithm based on density distribution of population

Ni Chen, Jun Zhang^*, Ou Liu

^*Corresponding author for this work

Operations & Information Management

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

The control parameters in evolutionary algorithms (EAs) have significant effects on the behavior and performance of the algorithm. Most existing parameter control mechanisms are based on either individual fitness or positional distribution of population. This paper proposes a parameter adaptation strategy which aims at evaluating the density distribution of population as well as both the fitness values comprehensively, and adapting the parameters accordingly. The proposed method modifies the values of px and pm based on the relative cluster density and the relative sizes of clusters containing the best and the worst individuals. Copyright is held by the author/owner(s).

Original language	English
Title of host publication	GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion
Publisher	ACM
Pages	1543-1544
Number of pages	2
ISBN (Print)	9781450311786
DOIs	https://doi.org/10.1145/2330784.2331039
Publication status	Published - 20 Aug 2012
Event	14th International Conference on Genetic and Evolutionary Computation, GECCO'12 - Philadelphia, PA, United States Duration: 7 Jul 2012 → 11 Jul 2012

Conference

Conference	14th International Conference on Genetic and Evolutionary Computation, GECCO'12
Country/Territory	United States
City	Philadelphia, PA
Period	7/07/12 → 11/07/12

Keywords

Evolutionary algorithms
Genetic algorithm
Parameter adaptation

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10.1145/2330784.2331039

Cite this

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title = "Adaptive genetic algorithm based on density distribution of population",

abstract = "The control parameters in evolutionary algorithms (EAs) have significant effects on the behavior and performance of the algorithm. Most existing parameter control mechanisms are based on either individual fitness or positional distribution of population. This paper proposes a parameter adaptation strategy which aims at evaluating the density distribution of population as well as both the fitness values comprehensively, and adapting the parameters accordingly. The proposed method modifies the values of px and pm based on the relative cluster density and the relative sizes of clusters containing the best and the worst individuals. Copyright is held by the author/owner(s).",

keywords = "Evolutionary algorithms, Genetic algorithm, Parameter adaptation",

author = "Ni Chen and Jun Zhang and Ou Liu",

year = "2012",

month = aug,

day = "20",

doi = "10.1145/2330784.2331039",

language = "English",

isbn = "9781450311786",

pages = "1543--1544",

booktitle = "GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion",

publisher = "ACM",

address = "United States",

note = "14th International Conference on Genetic and Evolutionary Computation, GECCO'12 ; Conference date: 07-07-2012 Through 11-07-2012",

}

Chen, N, Zhang, J & Liu, O 2012, Adaptive genetic algorithm based on density distribution of population. in GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion. ACM, pp. 1543-1544, 14th International Conference on Genetic and Evolutionary Computation, GECCO'12, Philadelphia, PA, United States, 7/07/12. https://doi.org/10.1145/2330784.2331039

TY - GEN

T1 - Adaptive genetic algorithm based on density distribution of population

AU - Chen, Ni

AU - Zhang, Jun

AU - Liu, Ou

PY - 2012/8/20

Y1 - 2012/8/20

N2 - The control parameters in evolutionary algorithms (EAs) have significant effects on the behavior and performance of the algorithm. Most existing parameter control mechanisms are based on either individual fitness or positional distribution of population. This paper proposes a parameter adaptation strategy which aims at evaluating the density distribution of population as well as both the fitness values comprehensively, and adapting the parameters accordingly. The proposed method modifies the values of px and pm based on the relative cluster density and the relative sizes of clusters containing the best and the worst individuals. Copyright is held by the author/owner(s).

AB - The control parameters in evolutionary algorithms (EAs) have significant effects on the behavior and performance of the algorithm. Most existing parameter control mechanisms are based on either individual fitness or positional distribution of population. This paper proposes a parameter adaptation strategy which aims at evaluating the density distribution of population as well as both the fitness values comprehensively, and adapting the parameters accordingly. The proposed method modifies the values of px and pm based on the relative cluster density and the relative sizes of clusters containing the best and the worst individuals. Copyright is held by the author/owner(s).

KW - Evolutionary algorithms

KW - Genetic algorithm

KW - Parameter adaptation

UR - http://www.scopus.com/inward/record.url?scp=84865047936&partnerID=8YFLogxK

UR - https://dl.acm.org/citation.cfm?doid=2330784.2331039

U2 - 10.1145/2330784.2331039

DO - 10.1145/2330784.2331039

M3 - Conference publication

AN - SCOPUS:84865047936

SN - 9781450311786

SP - 1543

EP - 1544

BT - GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion

PB - ACM

T2 - 14th International Conference on Genetic and Evolutionary Computation, GECCO'12

Y2 - 7 July 2012 through 11 July 2012

ER -

Adaptive genetic algorithm based on density distribution of population

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