Selection Methods to Relax Strict Acceptance Condition in Test-Based Coevolution

Abstract

The Population-based Pareto Hill Climber (P-PHC) algorithm exemplifies coevolutionary computation approaches that manage a group of candidate solutions both used as a population to explore the underlying search space as well as an archive preserving solutions that meet the adopted solution concept. In some circumstances when parsimonious evaluations are desired, inefficiencies can arise from using the same group of candidate solutions for both purposes. The reliance, in such algorithms, on the otherwise beneficial Pareto dominance concept can create bottlenecks on search progress as most newly generated solutions are non-dominated, and thus appear equally qualified to selection, when compared to the current ones they should eventually replace. We propose new selection conditions that include both Pareto dominated and Pareto non-dominated solutions, as well as other factors to help provide distinctions for selection. The potential benefits of also considering Pareto non-dominated solutions are illustrated by a visualization of the underlying interaction space in terms of levels. In addition, we define some new performance metrics that allow one to compare our various selection methods in terms of ideal evaluation of coevolution. Fewer duplicate solutions are retained in the final generation, thus allowing for more efficient usage of the fixed population size.