A coevolution archive based on problem dimension

Recent work has shown the existence of an implicit dimension structure within coevolution problems, which can uniquely determine the overall performance of a individual. In this paper, we present a reliable dimension identifying method. Based on this , we put forward a suitable archive scheme, which maintains only the most representative individuals in terms of problem dimensional information identified during coevolution, and achieves minimum size while guaranteeing monotonic progress. The experimental results on COMPARE-ON-ONE and COMPARE-ON-ALL demonstrate the viability of the algorithm