Why Playing Against Diverse and Challenging Opponents Speeds Up Coevolution: A Theoretical Analysis on Combinatorial Games
Alistair Benford, Per Kristian Lehre
Competitive coevolutionary algorithms (CoEAs) have a natural application to problems that are adversarial or feature strategic interaction. However, there is currently limited theoretical insight into how to avoid pathological behaviour associated to CoEAs. In this paper we use impartial combinatorial games as a challenging domain for CoEAs and provide corresponding runtime analysis. By analysing how individuals capitalise on the mistakes of their opponents, we prove that the Univariate Marginal Distribution Algorithm finds (with high probability) an optimal strategy for a game called Reciprocal LeadingOnes within $O(n^2\log^3{n})$ game evaluations, a significant improvement over the best known bound of $O(n^5\log^2{n})$. Critical to the analysis is the introduction of a novel stabilising operator, the impact of which we study both theoretically and empirically.