An Adaptive Strategy for Assortative Mating in Genetic Algorithm

In any traditional Genetic Algorithm (GA), recombination is a dominant search operator and capable of exploring the search space by sharing genetic information among the individuals in the population. However, a simple application of recombination alone is insufficient to guide convergence to an optimal solution. The selection of parents for recombination operation has a significant role in guiding the evolution towards the optimal solution and also for maintaining genetic diversity to avoid getting trapped in local minima. A non-random mating mimics the mechanism of reproduction in nature and is effective in maintaining diversity in population. This paper proposes a new strategy for selection of mating pairs based on a type of non-random mating called as assortative mating. The proposed mate selection scheme conserves the merits of both positive and negative assortative mating in a controlled manner by allowing mating between individuals having both similar and dissimilar phenotypes. For effective cross-over, it maintains genetic diversity in population by distributing the recombination among dissimilar individuals. Furthermore, it ensures the preservation and propagation of useful genetic information to the later stages of search by the selection of mates having similar phenotypes. Experimental results, using not only the five widely used benchmark functions but also twenty newly developed modified functions, are reported. The results show significant improvements in the convergence characteristics of the proposed mating strategy over existing nonrandom mating techniques.