All You Need is Sex for Diversity

Abstract

Maintaining genetic diversity as a means to avoid premature convergence is critical in Genetic Programming. Several approaches have been proposed to achieve this, with some focusing on the mating phase from coupling dissimilar solutions to some form of self-adaptive selection mechanism. In nature, genetic diversity can be the consequence of many di erent factors, but when considering reproduction Sexual Selection can have an impact on promoting variety within a species. Speci cally, Mate Choice often results in di erent selective pressures between sexes, which in turn may trigger evolutionary di erences among them. Although some mechanisms of Sexual Selection have been applied to Genetic Programming in the past, the literature is scarce when it comes to mate choice. Recently, a way of modelling mating preferences by ideal mate representations was proposed, achieving good results when compared to a standard approach. These mating preferences evolve freely in a self-adaptive fashion, creating an evolutionary driving force of its own alongside tness pressure. The inner mechanisms of this approach operate from personal choice, as each individual has its own representation of a perfect mate which a ects the mate to be selected. In this paper, we compare this method against a random mate choice to assess whether there are advantages in evolving personal preferences. We conducted experiments using three symbolic regression problems and di erent mutation rates. The results show that self-adaptive mating preferences are able to create a more diverse set of solutions when compared to the traditional approach and a random mate approach (with statistically signi cant di erences) and have a higher success rate in three of the six instances tested.