Accelerating coevolution with adaptive matrix factorization
Paweł Liskowski, Wojciech Jaśkowski · Proceedings of the Genetic and Evolutionary Computation Conference · 2017
Among many interaction schemes in coevolutionary settings for interactive domains, the round-robin tournament provides the most precise evaluation of candidate solutions at the expense of computational effort. In order to improve the coevolutionary learning speed, we propose an interaction scheme that computes only a fraction of interactions outcomes between the pairs of coevolving individuals. The missing outcomes in the interaction matrix are predicted using matrix factorization. The algorithm adaptively decides how much of the interaction matrix to compute based on the learning speed statistics. We evaluate our method in the context of coevolutionary covariance matrix adaptation strategy (CoCMAES) for the problem of learning position evaluation in the game of Othello. We show that our adaptive interaction scheme allows to match the state-of-the-art results obtained by the standard round-robin CoCMAES while, at the same time, considerably improves the learning speed.