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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.