Avoiding the pitfalls of noisy fitness functions with genetic algorithms
Fiacc Larkin, Conor Ryan · 2009
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.