The role of crossover in an immunity based genetic algorithm for multimodal function optimization

When genetic algorithms are employed in multimodal function optimization, identifying multiple peaks and maintaining subpopulations of the search space are two central themes. In this paper, we use an immune system model to explore the role of crossover in GAs with respect to these two issues. The experimental results reported here shed more light into how crossover affects the GA's search power in the context of multimodal function optimization. We also show that an adaptive crossover strategy successfully achieves the two goals simultaneously. These results on the effects of crossover are a step toward a deeper understanding of how GAs work, and thus how to design more robust GAs for solving multimodal optimization problems.