  We propose to use a quality estimator and evolutionary methods to search the
latent space of generative adversarial networks trained on small, difficult
datasets, or both. The new method leads to the generation of significantly
higher quality images while preserving the original generator's diversity.
Human raters preferred an image from the new version with frequency 83.7pc for
Cats, 74pc for FashionGen, 70.4pc for Horses, and 69.2pc for Artworks, and
minor improvements for the already excellent GANs for faces. This approach
applies to any quality scorer and GAN generator.
