Avoiding the pitfalls of noisy fitness functions with genetic algorithms

Authors:
Fiacc Larkin;Conor Ryan
Affiliations:
University of Limerick, Limerick, Ireland;University of Limerick, Limerick, Ireland
Venue:
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Year:
2009

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Abstract

We have examined the application of genetic Algorithms to noisy fitness functions and consider the accepted wisdom of sampling, or multiple evaluations of individuals, as a mechanism for identifying true performance. Given a large ( 10%) amount of noise, a standard GA of surprisingly modest population size outperforms a GA using sampling, when compared on fitness versus evaluations. We also document a detrimental phenomenon we term the Glass Ceiling, which is when individuals of high fitness become confused with individuals of perfect fitness by the GA. We pinpoint the precise conditions that create this effect.