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 different factors, but when
considering reproduction Sexual Selection can have an impact on promoting
variety within a species. Specifically, Mate Choice often results in different
selective pressures between sexes, which in turn may trigger evolutionary
differences 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 fitness
pressure. The inner mechanisms of this approach operate from personal choice,
as each individual has its own representation of a perfect mate which affects
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 different 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
significant differences) and have a higher success rate in three of the six
instances tested.

本文研究了基因编程中维护遗传多样性的方法，特别是将性选择中的选择配偶机制引入到基因编程中，比较了利用自适应选择的配偶模型和随机选择模型的优劣。结果表明，自适应选择模型可以更有效地创建多样化的解决方案，并在三个测试实例中具有更高的成功率。