Neuroevolution of Generative Adversarial Networks

Abstract

Generative Adversarial Networks (GAN) is an adversarial model that became relevant in the last years, displaying impressive results in generative tasks. A GAN combines two neural networks, a discriminator and a generator, trained in an adversarial way. The discriminator learns to distinguish between real samples of an input dataset and fake samples. The generator creates fake samples aiming to fool the discriminator. The training progresses iteratively, leading to the production of realistic samples that can mislead the discriminator. Despite the impressive results, GANs are hard to train, and a trial-and-error approach is generally used to obtain consistent results. Since the original GAN proposal, research has been conducted not only to improve the quality of the generated results but also to overcome the training issues and provide a robust training process. However, even with the advances in the GAN model, stability issues are still present in the training of GANs. Neuroevolution, the application of evolutionary algorithms in neural networks, was recently proposed as a strategy to train and evolve GANs. These proposals use the evolutionary pressure to guide the training of GANs to build robust models, leveraging the quality of results, and providing a more stable training. Furthermore, these proposals can automatically provide useful architectural definitions, avoiding the manual discovery of suitable models for GANs. We show the current advances in the use of evolutionary algorithms and GANs, presenting the state-of-the-art proposals related to this context. Finally, we discuss perspectives and possible directions for further advances in the use of evolutionary algorithms and GANs.