# EvolGAN: Evolutionary Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2009.13311 · HTML: https://mlsanity.com/paper/2009.13311/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [Evolutionary Generative Adversarial Networks with Crossover Based Knowledge Distillation](https://mlsanity.com/paper/2101.11186/) (2021)
- [Fairness in generative modeling](https://mlsanity.com/paper/2210.03517/) (2022)
- [Evolutionary Retrofitting](https://mlsanity.com/paper/2410.11330/) (2024)
- [IE-GAN: An Improved Evolutionary Generative Adversarial Network Using a New Fitness Function and a Generic Crossover Operator](https://mlsanity.com/paper/2109.11078/) (2021)
- [Collaborative Interactive Evolution of Art in the Latent Space of Deep Generative Models](https://mlsanity.com/paper/2403.19620/) (2024)
- [Evolutionary Computation as Natural Generative AI](https://mlsanity.com/paper/2510.08590/) (2025)
- [Uncovering the Generative Landscape of Diffusion Models: A Spectral-Guided Quality-Diversity Diffusion Framework](https://mlsanity.com/paper/2610.15724/) (2026)

## Abstract (arXiv)

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.