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Description:
While noise is a phenomenon present in many real-world optimization problems, the understanding of its potential effects on the performance of evolutionary algorithms is still incomplete. This paper investigates the effects of noise for the infinite-dimensional quadratic sphere and a (1 +1)-ES with isotropic normal mutations. It is shown that overvaluation as a result of failure to reevaluate parental fitness leads to both reduced success probabilities and improved performance. Implications for mutation strength adaptation rules are discussed and optimal resampling rates are computed.