Investigation of a cellular genetic algorithm that mimics landscape ecology

Michael Kirley, Xiaodong Li, David G. Green

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

16 Citations (Scopus)

Abstract

The cellular genetic algorithm (CGA) combines GAs with cellular automata by spreading an evolving population across a pseudo-landscape. In this study we use insights from ecology to introduce new features, such as disasters and connectivity changes, into the algorithm. We investigate the performance and behaviour of the algorithm on standard GA hard problems. The CGA has the advantage of avoiding premature convergence and outperforms standard GAs on particular problems. A potentially important feature of the algorithm’s behaviour is that the fitness of solutions frequently improves in large jumps following disturbances (culling by patches).

Original languageEnglish
Title of host publicationSimulated Evolution and Learning - 2nd Asia-Pacific Conference on Simulated Evolution and Learning, SEAL 1998, Selected Papers
EditorsBob McKay, Xin Yao, Charles S. Newton, Jong-Hwan Kim, Takeshi Furuhashi
PublisherSpringer
Pages90-97
Number of pages8
ISBN (Print)3540659072, 9783540659075
Publication statusPublished - 1999
Externally publishedYes
Event2nd Asia-Pacific Conference on Simulated Evolution and Learning, SEAL 1998 - Canberra, Australia
Duration: 24 Nov 199827 Nov 1998

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume1585
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd Asia-Pacific Conference on Simulated Evolution and Learning, SEAL 1998
CountryAustralia
CityCanberra
Period24/11/9827/11/98

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