Cellular Genetic Algorithms with Evolutional Rule

The cellular genetic algorithm (CGA) is growing which combines GAs with cellular automata. The individuals are distributed in a toridal grid or presudo landscape and their genetic operator is restricted to within neighborhood. In this paper, we present a cellular genetic algorithm with evolutional rule (CGAE) which more closely mimics the process of evolvement in ecology by cellular automata. It provides a mechanism for maintaining flexible population size and self-adaptive control on migration .We investigate the performance and behavior of the algorithm on complex problem like problems having high epistasis and multimodality. According to numerical optimization problems, CGAE is compared with other CGA. In the paper, the experiment result indicated that CGAE can improve convergent speed and maintain diversity of population. It shows that this novel algorithm has application prospects.