Evolutionary algorithms in noisy environments: theoretical issues and guidelines for practice Original Research Article

This paper is devoted to the effects of fitness noise in evolutionary algorithms (EAs). After a short introduction to the history of this research field, the performance of genetic algorithms (GAs) and evolution strategies (ESs) on the hyper-sphere test function is evaluated. It will be shown that the main effects of noise – the decrease of convergence velocity and the residual location error R∞ – are observed in both GAs and ESs. Different methods for improving the performance are presented and hypotheses on their working mechanisms are discussed. The method of rescaled mutations is analyzed in depth for the (1,λ)-ES on the sphere model. It is shown that this method needs advanced self-adaptation (SA) techniques in order to take advantage of the theoretically predicted performance gain. The troubles with current self-adaptation techniques are discussed and directions for further research will be worked out. Article Outli