Using an Immune System Model to Explore Mate Selection in Genetic Algorithms

When Genetic Algorithms (GAs) are employed in multimodal function optimization, engineering and machine learning, identifying multiple peaks and maintaining subpopulations of the search space are two central themes. In this paper, an immune system model is adopted to develop a framework for exploring the role of mate selection in GAs with respect to these two issues. The experimental results reported in the paper will shed more light into how mate selection schemes compare to traditional selection schemes. In particular, we show that dissimilar mating is beneficial in identifying multiple peaks, yet harmful in maintaining subpopulations of the search space.