Promoting diversity using migration strategies in distributed genetic algorithms

David Power; Conor Ryan; R. Muhammed Atif Azad

Promoting diversity using migration strategies in distributed genetic algorithms

David Power, Conor Ryan, R. Muhammed Atif Azad

Department of Computer Science & Information Systems

University of Limerick

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper presents a new migration strategy that improves the overall quality of solutions in a distributed genetic algorithm (DGA involving a number of concurrently evolving populations. The idea behind this improvement is to incorporate a diversity guided selection mechanism that selects a diverse set of individuals for migration from the evolving populations. To accompany this selection mechanism an alternative replacement policy which replaces individuals that have more than one of their copies present in the population (clones) is also investigated. This increases diversity within a population and reduces premature convergence. Results show that it leads to a better performance when compared with the send-best-replace-worst strategy.

Original language	English
Title of host publication	2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
Pages	1831-1838
Number of pages	8
Publication status	Published - 2005
Event	2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 - Edinburgh, Scotland, United Kingdom Duration: 2 Sep 2005 → 5 Sep 2005

Publication series

Name	2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
Volume	2

Conference

Conference	2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005
Country/Territory	United Kingdom
City	Edinburgh, Scotland
Period	2/09/05 → 5/09/05

Cite this

@inproceedings{debf6742bbea4fb2bd185c25a3417663,

title = "Promoting diversity using migration strategies in distributed genetic algorithms",

abstract = "This paper presents a new migration strategy that improves the overall quality of solutions in a distributed genetic algorithm (DGA involving a number of concurrently evolving populations. The idea behind this improvement is to incorporate a diversity guided selection mechanism that selects a diverse set of individuals for migration from the evolving populations. To accompany this selection mechanism an alternative replacement policy which replaces individuals that have more than one of their copies present in the population (clones) is also investigated. This increases diversity within a population and reduces premature convergence. Results show that it leads to a better performance when compared with the send-best-replace-worst strategy.",

author = "David Power and Conor Ryan and Azad, {R. Muhammed Atif}",

year = "2005",

language = "English",

isbn = "0780393635",

series = "2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings",

pages = "1831--1838",

booktitle = "2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings",

note = "2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 ; Conference date: 02-09-2005 Through 05-09-2005",

}

Power, D, Ryan, C & Azad, RMA 2005, Promoting diversity using migration strategies in distributed genetic algorithms. in 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings. 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings, vol. 2, pp. 1831-1838, 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005, Edinburgh, Scotland, United Kingdom, 2/09/05.

Promoting diversity using migration strategies in distributed genetic algorithms. / Power, David; Ryan, Conor; Azad, R. Muhammed Atif.
2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings. 2005. p. 1831-1838 (2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Azad, R. Muhammed Atif

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N2 - This paper presents a new migration strategy that improves the overall quality of solutions in a distributed genetic algorithm (DGA involving a number of concurrently evolving populations. The idea behind this improvement is to incorporate a diversity guided selection mechanism that selects a diverse set of individuals for migration from the evolving populations. To accompany this selection mechanism an alternative replacement policy which replaces individuals that have more than one of their copies present in the population (clones) is also investigated. This increases diversity within a population and reduces premature convergence. Results show that it leads to a better performance when compared with the send-best-replace-worst strategy.

AB - This paper presents a new migration strategy that improves the overall quality of solutions in a distributed genetic algorithm (DGA involving a number of concurrently evolving populations. The idea behind this improvement is to incorporate a diversity guided selection mechanism that selects a diverse set of individuals for migration from the evolving populations. To accompany this selection mechanism an alternative replacement policy which replaces individuals that have more than one of their copies present in the population (clones) is also investigated. This increases diversity within a population and reduces premature convergence. Results show that it leads to a better performance when compared with the send-best-replace-worst strategy.

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T3 - 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings

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Y2 - 2 September 2005 through 5 September 2005

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Promoting diversity using migration strategies in distributed genetic algorithms

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