An Efficient Coevolutionary Algorithm Using Dynamic Species Control

A coevolutionary algorithm is an extention of the conventional genetic algorithm that incorporates the strategy of divide and conquer in developing a complex solution in the form of interacting co-adapted subcomponents. In this paper we propose an efficient coevolutionary algorithm dynamically controlling species splitting and merging. Our algorithm conducts efficient local search in the reduced search space by splitting species for independent variables while it conducts global search by merging species for interdependent variables. We have experimented the proposed algorithm with some benchmarking function optimization problems and the inventory control problem, and have shown that the algorithm outperforms the existing coevolutionary algorithms.