Towards a bounded Pareto-coevolution archive

Abstract

Convolution offers adaptive methods for the selection of tests used to evaluate individuals, but the resulting evaluation can be unstable. Recently, general archive-based coevolution methods have become available for which monotonic progress can be guaranteed. The size of these archives may grow indefinitely however, thus limiting their application potential. Here, we investigate how the size of an archive for Pareto-coevolution may be limited while maintaining reliability. The LAyered Pareto-Coevolution Archive (LAPCA) is presented, and investigated in experiments. LAPCA features a tunable degree of reliability, and is found to provide reliable progress in a difficult test problem while maintaining approximately constant archive sizes.