Arnold, Dirk V.Beyer, Hans-Georg2004-12-072004-12-0720002001-10-16http://hdl.handle.net/2003/538410.17877/DE290R-15271While 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.enUniversität DortmundReihe Computational Intelligence ; 80evolution strategylocal performancenoiseovervaluation004report