Studies on Pareto-based multi-objective Competitive Coevolutionary Dynamics
Faculty of Computing, Health and Science
School of Computer and Security Science / Artificial Intelligence and Optimisation Research Centre
Competitive coevolutionary algorithms are stochastic population-based search algorithms. To date, most competitive coevolution research has been carried in the domain of single-objective optimization. We propose a novel competitive coevolutionary framework to explore Pareto-based multiobjective competitive coevolution. This framework utilizes the hypervolume indicator and fitness sharing mechanism to address disengagement and over-specialisation issues. A diversity-driven evolutionary selection scheme is utilized to deal with the loss of fitness gradient problem. Several series of experiments are conducted using multi-objective two-sided competitive games. The results suggest that Pareto-optimal solutions can effectively be found using our proposed coevolutionary framework.