Performance evaluation of combined cellular genetic algorithms for function optimization problems

In this paper, we evaluate the performance of combined cellular genetic algorithms for function optimization problems. There are multiple subpopulations that have cellular structures in the combined cellular genetic algorithm. The subpopulations interact with each other only at their edges. We have already showed the high performance of the combined cellular genetic algorithms over other distributed genetic algorithms such as the standard cellular genetic algorithms and the island genetic algorithm. This paper examines the effects of parameter specifications such as the number of the subpopulations, the way of placing elite individuals, and the topology of the subpopulations on the performance of the combined cellular genetic algorithms. We perform computer simulations on function optimization problems that are well known in the literature.