We propose a computational model which includes adaptive mutation and sexual selection, and investigate functional aspects of the model by examining self-adaptation processes of mutation under the sexual selection. In mating, each female observes males' traits in a phenotypic space, then chooses a male according to her own preference. Therefore, the selection pressure for males is different from that for females. This method was applied for the maximum search problem of a 2-dimensional complicated function. The simulation result showed that the proposed method can escape local optima by the runaway effect of males' traits and females' preferences, and this process leads the system to the intermittent evolution. In this phase, an asymmetrical mutation was driven, which provided the different roles of searching strategies depending on sex. The females were conservative with low mutation rates, while the males were innovative with higher mutation rates. As a result, the proposed method was able to search larger maxima than the conventional methods.
