A Simple Coevolution Archive Based on Bidirectional Dimension Extraction

Abstract

A common technique in coevolution aimed at improving reliability is the use of an archive. Recent research has shown that within coevolutionary problem domains, there is an implicit set of informative dimensions which structures the evaluation space for individuals. According to above research, in this paper we propose a novel archive algorithm, which adopts a simple and feasible dimension extracting method, extracts dimension systems from both candidates and tests sides respectively, and select only high-performance individuals representing informative dimensions to reside in archives. Therefore, the proposed algorithm can prevent archives from growing overly larger while maintaining progress. Furthermore, as the conditions of dimension extraction are reduced, it is more widely applicable than other similar methods. Experimental results on test problems demonstrate the feasibility and validity of the proposed algorithm.