  Distributed coevolutionary Generative Adversarial Network (GAN) training has
empirically shown success in overcoming GAN training pathologies. This is
mainly due to diversity maintenance in the populations of generators and
discriminators during the training process. The method studied here coevolves
sub-populations on each cell of a spatial grid organized into overlapping Moore
neighborhoods. We investigate the impact on the performance of two algorithm
components that influence the diversity during coevolution: the
performance-based selection/replacement inside each sub-population and the
communication through migration of solutions (networks) among overlapping
neighborhoods. In experiments on MNIST dataset, we find that the combination of
these two components provides the best generative models. In addition,
migrating solutions without applying selection in the sub-populations achieves
competitive results, while selection without communication between cells
reduces performance.
