# Spatial Evolutionary Generative Adversarial Networks

## TL;DR

This paper introduces Mustangs, a novel evolutionary GAN training method that combines mutation and population diversity strategies, leading to faster and more accurate generative models on benchmarks like MNIST and CelebA.

## Contribution

Mustangs innovatively integrates mutation and population diversity approaches, replacing the single loss function with multiple, improving training stability and performance.

## Key findings

- Mustangs trains faster than existing methods.
- Mustangs produces more accurate generative models.
- Experimental results show statistical improvements on benchmarks.

## Abstract

Generative adversary networks (GANs) suffer from training pathologies such as instability and mode collapse. These pathologies mainly arise from a lack of diversity in their adversarial interactions. Evolutionary generative adversarial networks apply the principles of evolutionary computation to mitigate these problems. We hybridize two of these approaches that promote training diversity. One, E-GAN, at each batch, injects mutation diversity by training the (replicated) generator with three independent objective functions then selecting the resulting best performing generator for the next batch. The other, Lipizzaner, injects population diversity by training a two-dimensional grid of GANs with a distributed evolutionary algorithm that includes neighbor exchanges of additional training adversaries, performance based selection and population-based hyper-parameter tuning. We propose to combine mutation and population approaches to diversity improvement. We contribute a superior evolutionary GANs training method, Mustangs, that eliminates the single loss function used across Lipizzaner's grid. Instead, each training round, a loss function is selected with equal probability, from among the three E-GAN uses. Experimental analyses on standard benchmarks, MNIST and CelebA, demonstrate that Mustangs provides a statistically faster training method resulting in more accurate networks.