  Generative adversarial networks (GAN) present state-of-the-art results in the
generation of samples following the distribution of the input dataset. However,
GANs are difficult to train, and several aspects of the model should be
previously designed by hand. Neuroevolution is a well-known technique used to
provide the automatic design of network architectures which was recently
expanded to deep neural networks. COEGAN is a model that uses neuroevolution
and coevolution in the GAN training algorithm to provide a more stable training
method and the automatic design of neural network architectures. COEGAN makes
use of the adversarial aspect of the GAN components to implement coevolutionary
strategies in the training algorithm. Our proposal was evaluated in the
Fashion-MNIST and MNIST dataset. We compare our results with a baseline based
on DCGAN and also with results from a random search algorithm. We show that our
method is able to discover efficient architectures in the Fashion-MNIST and
MNIST datasets. The results also suggest that COEGAN can be used as a training
algorithm for GANs to avoid common issues, such as the mode collapse problem.
