Selection procedures for module discovery : exploring evolutionary algorithms for cognitive science

, , , , & (2001) Selection procedures for module discovery : exploring evolutionary algorithms for cognitive science. In Moore, Johanna D. & Stenning, Keith (Eds.) Proceedings of the Twenty-Third Annual Conference of the Cognitive Science Society, Edinburgh, Scotland, pp. 1124-1129.

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Abstract

Evolutionary algorithms are playing an increasingly important role as search methods in cognitive science domains. In this study, methodological issues in the use of evolutionary algorithms were investigated via simulations in which procedures were systematically varied to modify the selection pressures on populations of evolving agents. Traditional roulette wheel, tournament, and variations of these selection algorithms were compared on the “needle-in-a-haystack” problem developed by Hinton and Nowlan in their 1987 study of the Baldwin effect. The task is an important one for cognitive science, as it demonstrates the power of learning as a local search technique in smoothing a fitness landscape that lacks gradient information. One aspect that has continued to foster interest in the problem is the observation of residual learning ability in simulated populations even after long periods of time.

Effective evolutionary algorithms balance their search effort between broad exploration of the search space and in-depth exploitation of promising solutions already found. Issues discussed include the differential effects of rank and proportional selection, the tradeoff between migration of populations towards good solutions and maintenance of diversity, and the development of measures that illustrate how each selection algorithm affects the search process over generations. We show that both roulette wheel and tournament algorithms can be modified to appropriately balance search between exploration and exploitation, and effectively eliminate residual learning in this problem.

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ID Code: 79339
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Evolutionary algorithms, Evolutionary computation
ISBN: 0805841520
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2001 by the Cognitive Science Society
Deposited On: 04 Jan 2015 23:56
Last Modified: 05 Jan 2015 20:39

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