Empirical analysis of the spatial genetic algorithm on small-world networks

Abstract

Genetic algorithm (GA) has been widely used in optimizing and solving various problems since first proposed, and its characters also have been deeply studied. In this paper, we investigate the benefits of genetic algorithm whose population is distributed on small-world networks. In particular, we pay our attention to the complexity of how small-world affects the behavior of spatial GA. Our work shows that, on a complex problem, the behavior of spatial GA on the small-world networks is influenced by at least two different factors: local selection and asymmetric topology. It is more complex than previous results from simple lattice models. Our results could provide lots of potential methods to improve the performance of spatial GA and give some guidance for designing of parallel genetic algorithm. We also present many future problems on the influence of small-world to spatial GA. © Springer-Verlag Berlin Heidelberg 2006.