Beyer, Hans-GeorgKalyanmoy Deb2004-12-072004-12-0719992001-10-16http://hdl.handle.net/2003/537410.17877/DE290R-15381Due to the exibility in adapting to different fitness landscapes, self-adaptive evolutionary algorithms (SA-EAs) have been gaining popularity in the recent past. In this paper, we postulate the properties that SA-EA operators should have for successful applications. Specifically, population mean and variance of a number of SA-EA operators, such as various real-parameter crossover operators and self-adaptive evolution strategies, are calculated for this purpose. In every case, simulation results are shown to verify the theoretical calculations. The postulations and population variance calculations explain why self-adaptive GAs and ESs have shown similar performance in the past and also suggest appropriate strategy parameter values which must be chosen while applying and comparing different SA-EAs.enUniversität DortmundReihe Computational Intelligence ; 69blend crossover operatorevolution strategiesfuzzy recombination operatorgenetic algorithmspopulation meanpopulation varianceself-adaptationsimulated binary crossover004report