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Evolutionary Computation

Summer 1996, Vol. 4, No. 2, Pages 113-131
(doi: 10.1162/evco.1996.4.2.113)
© 1996 by the Massachusetts Institute of Technology
Genetic Algorithms, Selection Schemes, and the Varying Effects of Noise
Article PDF (1.24 MB)
Abstract

This paper analyzes the effect of noise on different selection mechanisms for genetic algorithms (GAs). Models for several selection schemes are developed that successfully predict the convergence characteristics of GAs within noisy environments. The selection schemes modeled in this paper include proportionate selection, tournament selection, (μ, λ) selection, and linear ranking selection. An allele-wise model for convergence in the presence of noise is developed for the OneMax domain, and then extended to more complex domains where the building blocks are uniformly scaled. These models are shown to accurately predict the convergence rate of GAs for a wide range of noise levels.