Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
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In applications of genetic algorithms (GA) to real world problems, we often encounter significant amounts of noise in our fitness functions. We show that the interaction of normally-distributed noise and selection pressure inherently cause overestimation of GA population fitness. We call this inherent fitness overestimation noise pressure. Furthermore, we show that oversampling is not a sufficient technique for eliminating noise pressure.