  Generative adversarial networks (GAN) have been effective for learning
generative models for real-world data. However, existing GANs (GAN and its
variants) tend to suffer from training problems such as instability and mode
collapse. In this paper, we propose a novel GAN framework called evolutionary
generative adversarial networks (E-GAN) for stable GAN training and improved
generative performance. Unlike existing GANs, which employ a pre-defined
adversarial objective function alternately training a generator and a
discriminator, we utilize different adversarial training objectives as mutation
operations and evolve a population of generators to adapt to the environment
(i.e., the discriminator). We also utilize an evaluation mechanism to measure
the quality and diversity of generated samples, such that only well-performing
generator(s) are preserved and used for further training. In this way, E-GAN
overcomes the limitations of an individual adversarial training objective and
always preserves the best offspring, contributing to progress in and the
success of GANs. Experiments on several datasets demonstrate that E-GAN
achieves convincing generative performance and reduces the training problems
inherent in existing GANs.
