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Many practical optimization problems are subject to uncertain fitness evaluations. One way to reduce the noise is to average over multiple samples of the fitness function in order to evaluate a single individual. This paper proposes a general way to integrate statistical ranking and selection procedures into evolutionary algorithms. The proposed procedure focuses sampling on those individuals that are crucial for the evolutionary algorithm, and distributes samples in a way that efficiently reduces uncertainty. The goal is to drastically reduce the number of evaluations required for a proper operation of the evolutionary algorithm in noisy environments.