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Generative adversary networks (GANs) suffer from training pathologies such asinstability and mode collapse. These pathologies mainly arise from a lack ofdiversity in their adversarial interactions. Evolutionary generativeadversarial networks apply the principles of evolutionary computation tomitigate these problems. We hybridize two of these approaches that promotetraining diversity. One, E-GAN, at each batch, injects mutation diversity bytraining the (replicated) generator with three independent objective functionsthen selecting the resulting best performing generator for the next batch. Theother, Lipizzaner, injects population diversity by training a two-dimensionalgrid of GANs with a distributed evolutionary algorithm that includes neighborexchanges of additional training adversaries, performance based selection andpopulation-based hyper-parameter tuning. We propose to combine mutation andpopulation approaches to diversity improvement. We contribute a superiorevolutionary GANs training method, Mustangs, that eliminates the single lossfunction used across Lipizzaner's grid. Instead, each training round, a lossfunction is selected with equal probability, from among the three E-GAN uses.Experimental analyses on standard benchmarks, MNIST and CelebA, demonstratethat Mustangs provides a statistically faster training method resulting in moreaccurate networks.

Spatial Evolutionary Generative Adversarial Networks