# Evolutionary Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1803.00657 · HTML: https://mlsanity.com/paper/1803.00657/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2018.

## Built on by (most cited first, 30 of 42)

- [Modeling Image Composition for Complex Scene Generation](https://mlsanity.com/paper/2206.00923/) (2022)
- [Recent Advances for Quantum Neural Networks in Generative Learning](https://mlsanity.com/paper/2206.03066/) (2022)
- [Robust Unlearnable Examples: Protecting Data Against Adversarial Learning](https://mlsanity.com/paper/2203.14533/) (2022)
- [ViTAS: Vision Transformer Architecture Search](https://mlsanity.com/paper/2106.13700/) (2021)
- [A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU](https://mlsanity.com/paper/2305.17473/) (2023)
- [A Comprehensive Survey on Data-Efficient GANs in Image Generation](https://mlsanity.com/paper/2204.08329/) (2022)
- [FakeCLR: Exploring Contrastive Learning for Solving Latent Discontinuity in Data-Efficient GANs](https://mlsanity.com/paper/2207.08630/) (2022)
- [Face De-identification: State-of-the-art Methods and Comparative Studies](https://mlsanity.com/paper/2411.09863/) (2024)
- [A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization](https://mlsanity.com/paper/2206.11526/) (2022)
- [RLocator: Reinforcement Learning for Bug Localization](https://mlsanity.com/paper/2305.05586/) (2023)
- [EvoGAN: An Evolutionary Computation Assisted GAN](https://mlsanity.com/paper/2110.11583/) (2021)
- [EGANS: Evolutionary Generative Adversarial Network Search for Zero-Shot Learning](https://mlsanity.com/paper/2308.09915/) (2023)
- [EAGAN: Efficient Two-stage Evolutionary Architecture Search for GANs](https://mlsanity.com/paper/2111.15097/) (2021)
- [A Generative Adversarial Network for Climate Tipping Point Discovery (TIP-GAN)](https://mlsanity.com/paper/2302.10274/) (2023)
- [Peer is Your Pillar: A Data-unbalanced Conditional GANs for Few-shot Image Generation](https://mlsanity.com/paper/2311.08217/) (2023)
- [ScaleNet: Searching for the Model to Scale](https://mlsanity.com/paper/2207.07267/) (2022)
- [Leveraging GAN Priors for Few-Shot Part Segmentation](https://mlsanity.com/paper/2207.13428/) (2022)
- [Signal Propagation in a Gradient-Based and Evolutionary Learning System](https://mlsanity.com/paper/2102.08929/) (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)
- [A Data-driven Approach to Neural Architecture Search Initialization](https://mlsanity.com/paper/2111.03524/) (2021)
- [Multi-Fake Evolutionary Generative Adversarial Networks for Imbalance Hyperspectral Image Classification](https://mlsanity.com/paper/2111.04019/) (2021)
- [OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System](https://mlsanity.com/paper/2303.00501/) (2023)
- [CollagePrompt: A Benchmark for Budget-Friendly Visual Recognition with GPT-4V](https://mlsanity.com/paper/2403.11468/) (2024)
- [Evolving SimGANs to Improve Abnormal Electrocardiogram Classification](https://mlsanity.com/paper/2205.10116/) (2022)
- [See Through the Fog: Curriculum Learning with Progressive Occlusion in Medical Imaging](https://mlsanity.com/paper/2306.15574/) (2023)
- [MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking](https://mlsanity.com/paper/2505.24026/) (2025)
- [Fostering Diversity in Spatial Evolutionary Generative Adversarial Networks](https://mlsanity.com/paper/2106.13590/) (2021)
- [Parallelized Reverse Curriculum Generation](https://mlsanity.com/paper/2108.02128/) (2021)
- [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)

## Abstract (arXiv)

Generative adversarial networks (GAN) have been effective for learning generative models for real-world data. However, existing GANs (GAN and its variants) tend to suffer from training problems such as instability and mode collapse. In this paper, we propose a novel GAN framework called evolutionary generative adversarial networks (E-GAN) for stable GAN training and improved generative performance. Unlike existing GANs, which employ a pre-defined adversarial objective function alternately training a generator and a discriminator, we utilize different adversarial training objectives as mutation operations and evolve a population of generators to adapt to the environment (i.e., the discriminator). We also utilize an evaluation mechanism to measure the quality and diversity of generated samples, such that only well-performing generator(s) are preserved and used for further training. In this way, E-GAN overcomes the limitations of an individual adversarial training objective and always preserves the best offspring, contributing to progress in and the success of GANs. Experiments on several datasets demonstrate that E-GAN achieves convincing generative performance and reduces the training problems inherent in existing GANs.