  Generative adversarial networks (GANs) are widely used to learn generative
models. GANs consist of two networks, a generator and a discriminator, that
apply adversarial learning to optimize their parameters. This article presents
a parallel/distributed implementation of a cellular competitive coevolutionary
method to train two populations of GANs. A distributed memory parallel
implementation is proposed for execution in high performance/supercomputing
centers. Efficient results are reported on addressing the generation of
handwritten digits (MNIST dataset samples). Moreover, the proposed
implementation is able to reduce the training times and scale properly when
considering different grid sizes for training.
