# COEGAN: Evaluating the Coevolution Effect in Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1912.06180 · HTML: https://mlsanity.com/paper/1912.06180/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2019.

## Built on by (most cited first, 13 of 13)

- [CycleGAN for Undamaged-to-Damaged Domain Translation for Structural Health Monitoring and Damage Detection](https://mlsanity.com/paper/2202.07831/) (2022)
- [EGANS: Evolutionary Generative Adversarial Network Search for Zero-Shot Learning](https://mlsanity.com/paper/2308.09915/) (2023)
- [Generative Adversarial Networks for Labeled Acceleration Data Augmentation for Structural Damage Detection](https://mlsanity.com/paper/2112.03478/) (2021)
- [Evolutionary Generative Adversarial Networks with Crossover Based Knowledge Distillation](https://mlsanity.com/paper/2101.11186/) (2021)
- [Automating Generative Deep Learning for Artistic Purposes: Challenges and Opportunities](https://mlsanity.com/paper/2107.01858/) (2021)
- [Generative Adversarial Networks for Labelled Vibration Data Generation](https://mlsanity.com/paper/2112.08195/) (2021)
- [Generative Adversarial Networks for Data Generation in Structural Health Monitoring](https://mlsanity.com/paper/2112.08196/) (2021)
- [Evolving SimGANs to Improve Abnormal Electrocardiogram Classification](https://mlsanity.com/paper/2205.10116/) (2022)
- [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)
- [Evolutionary Computation as Natural Generative AI](https://mlsanity.com/paper/2510.08590/) (2025)
- [Image Synthesis Using Spintronic Deep Convolutional Generative Adversarial Network](https://mlsanity.com/paper/2601.01441/) (2026)
- [Neural Architecture Search for Generative Adversarial Networks: A Comprehensive Review and Critical Analysis](https://mlsanity.com/paper/2606.26169/) (2026)
- [Population-Based Multi-Objective Training of Discriminators for Semi-Supervised GANs](https://mlsanity.com/paper/2607.01907/) (2026)

## Abstract (arXiv)

Generative adversarial networks (GAN) present state-of-the-art results in the generation of samples following the distribution of the input dataset. However, GANs are difficult to train, and several aspects of the model should be previously designed by hand. Neuroevolution is a well-known technique used to provide the automatic design of network architectures which was recently expanded to deep neural networks. COEGAN is a model that uses neuroevolution and coevolution in the GAN training algorithm to provide a more stable training method and the automatic design of neural network architectures. COEGAN makes use of the adversarial aspect of the GAN components to implement coevolutionary strategies in the training algorithm. Our proposal was evaluated in the Fashion-MNIST and MNIST dataset. We compare our results with a baseline based on DCGAN and also with results from a random search algorithm. We show that our method is able to discover efficient architectures in the Fashion-MNIST and MNIST datasets. The results also suggest that COEGAN can be used as a training algorithm for GANs to avoid common issues, such as the mode collapse problem.