Improving Evolutionary Generative Adversarial Networks

Generative adversarial network (GAN) is a powerful method to reproduce the distribution of a given data set. It is widely used for generating photo-realistic images or data collections that appear real. Evolutionary GAN (E-GAN) is one of state-of-the-art GAN variations. E-GAN combines population based search and evolutionary operators from evolutionary algorithms with GAN to enhance diversity and search performance. In this study we aim to improve E-GAN by adding transfer learning and crossover which is a key evolutionary operator that is commonly used in evolutionary algorithms, but not in E-GAN.