# Deep Interactive Evolution

arXiv: https://arxiv.org/abs/1801.08230 · HTML: https://mlsanity.com/paper/1801.08230/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2018.

## Built on by (most cited first, 14 of 14)

- [ChatGPT and Other Large Language Models as Evolutionary Engines for Online Interactive Collaborative Game Design](https://mlsanity.com/paper/2303.02155/) (2023)
- [Master Face Attacks on Face Recognition Systems](https://mlsanity.com/paper/2109.03398/) (2021)
- [Lode Encoder: AI-constrained co-creativity](https://mlsanity.com/paper/2308.01312/) (2023)
- [PuzzleJAX: A Benchmark for Reasoning and Learning](https://mlsanity.com/paper/2508.16821/) (2025)
- [Using Multiple Generative Adversarial Networks to Build Better-Connected Levels for Mega Man](https://mlsanity.com/paper/2102.00337/) (2021)
- [Aesthetic Bot: Interactively Evolving Game Maps on Twitter](https://mlsanity.com/paper/2208.05017/) (2022)
- [Evolutionary Retrofitting](https://mlsanity.com/paper/2410.11330/) (2024)
- [Controlling the image generation process with parametric activation functions](https://mlsanity.com/paper/2510.15778/) (2025)
- [Evolutionary Machine Learning and Games](https://mlsanity.com/paper/2311.16172/) (2023)
- [Collaborative Interactive Evolution of Art in the Latent Space of Deep Generative Models](https://mlsanity.com/paper/2403.19620/) (2024)
- [HAIFAI: Human-AI Interaction for Mental Face Reconstruction](https://mlsanity.com/paper/2412.06323/) (2024)
- [Text-to-Level Diffusion Models With Various Text Encoders for Super Mario Bros](https://mlsanity.com/paper/2507.00184/) (2025)
- [Diffusion Crossover: Defining Evolutionary Recombination in Diffusion Models via Noise Sequence Interpolation](https://mlsanity.com/paper/2604.14790/) (2026)
- [Uncovering the Generative Landscape of Diffusion Models: A Spectral-Guided Quality-Diversity Diffusion Framework](https://mlsanity.com/paper/2610.15724/) (2026)

## Abstract (arXiv)

This paper describes an approach that combines generative adversarial networks (GANs) with interactive evolutionary computation (IEC). While GANs can be trained to produce lifelike images, they are normally sampled randomly from the learned distribution, providing limited control over the resulting output. On the other hand, interactive evolution has shown promise in creating various artifacts such as images, music and 3D objects, but traditionally relies on a hand-designed evolvable representation of the target domain. The main insight in this paper is that a GAN trained on a specific target domain can act as a compact and robust genotype-to-phenotype mapping (i.e. most produced phenotypes do resemble valid domain artifacts). Once such a GAN is trained, the latent vector given as input to the GAN's generator network can be put under evolutionary control, allowing controllable and high-quality image generation. In this paper, we demonstrate the advantage of this novel approach through a user study in which participants were able to evolve images that strongly resemble specific target images.