Fitness noise and localization errors of the optimum in general quadratic fitness models
Hans-Georg Beyer, Dirk V. Arnold · 1999
Evolutionary algorithms are generally believed to perform well in the presence of noise. Thus, they are often used for optimization in noisy environments. It comes as a surprise that hardly more than a handful of studies has dealt with the question of just how well they are doing and what can be done to improve their performance. The present paper presents empirical results regarding the behavior of genetic algorithms and evolution strategies in the presence of tness noise for a range of objective functions. Bounds for the mean residual location error and the stationary tness error for optimization of general quadratic tness models with evolution strategies are derived and compared with measurements. 1