SexualGA: Gender-Specifc Selection for Genetic Algorithms

Selection for reproduction in the context of Genetic Algorithms uses only one selection scheme to select parent individuals. When considering the model of sexual selection in the area of population genetics it gets obvious that the process of choosing mating partners in natural populations is different for male and female individuals. In this paper the authors introduce a new selection paradigm for Genetic Algorithms (SexualGA) based upon the concepts of male vigor and female choice of population genetics which provides the possibility to use two different selection schemes simultaneously within one algorithm. By using this new concept it is possible to simulate sexual selection in natural populations more precisely. Furthermore, SexualGA also offers far more flexibility concerning the adaptivity of selection pressure enabling the GA user to tune the algorithm more accurately.