@InProceedings{Liskowski2017accelerating,
  author =       {Paweł Liskowski and Wojciech Jaśkowski},
  title =        {Accelerating Coevolution with Adaptive Matrix Factorization},
  booktitle =    {GECCO'17: Proceedings of the 19th annual conference on Genetic and Evolutionary Computation},
  year =         {2017},
  address =      {Berlin, Germany},
  month =        {July},
  organization = {ACM},
  publisher =    {ACM Press},
  abstract =     {Abstract. 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 outcomes only for a fraction of interactions between the pairs of coevolving individuals. The missing outcomes in the interaction matrix are predicted by matrix factorization. Our 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 matching the state-of-the-art results obtained by the standard round-robin CoCMAES while, at the same time, considerably improving the learning speed.},
  journal =      {IEEE Transactions on Computational Intelligence and AI in Games},
  owner =        {Wojciech}
}