Abstract:

Among the existing research, most of evolution rules in cellular genetic algorithm （CGA） are directly introduced from cellular automaton. For these evolution rules, the interaction between individuals and the relationship between evolution scheme and group behavior of individuals are ignored. A new evolution CGA based on density dependence scheme is proposed to solve dynamic optimization problems, in which state evolution is achieved by density dependence and intraspecific competition. Moreover, a growth model in fixed cellular space is also proposed to control the population size in the evolutionary process. Dynamic optimization problems with different complexity are selected to verify the algorithm performance. The computation results indicate that the new algorithm has the approving performance in dealing with the dynamic optimization problems.