This paper is devoted to the effects of fitness noise in EAs (evolutionary 
 algorithms). After a short introduction to the history of this research 
 field, the performance of GAs (genetic algorithms) and ESs (evolution 
 strategies) 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 $R_\infty$ -- 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 $(1,\lambda)$-ES on the sphere model. 
 It is shown that this method needs advanced self-adaptation 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.