# Interactive Evolution and Exploration Within Latent Level-Design Space of Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2004.00151 · HTML: https://mlsanity.com/paper/2004.00151/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

## Built on by (most cited first, 22 of 22)

- [Constrained Graphic Layout Generation via Latent Optimization](https://mlsanity.com/paper/2108.00871/) (2021)
- [Level Generation Through Large Language Models](https://mlsanity.com/paper/2302.05817/) (2023)
- [Generating and Blending Game Levels via Quality-Diversity in the Latent Space of a Variational Autoencoder](https://mlsanity.com/paper/2102.12463/) (2021)
- [ChatGPT and Other Large Language Models as Evolutionary Engines for Online Interactive Collaborative Game Design](https://mlsanity.com/paper/2303.02155/) (2023)
- [Illuminating the Space of Beatable Lode Runner Levels Produced By Various Generative Adversarial Networks](https://mlsanity.com/paper/2101.07868/) (2021)
- [Dungeon and Platformer Level Blending and Generation using Conditional VAEs](https://mlsanity.com/paper/2106.12692/) (2021)
- [TaikoNation: Patterning-focused Chart Generation for Rhythm Action Games](https://mlsanity.com/paper/2107.12506/) (2021)
- [Lode Encoder: AI-constrained co-creativity](https://mlsanity.com/paper/2308.01312/) (2023)
- [Mario Plays on a Manifold: Generating Functional Content in Latent Space through Differential Geometry](https://mlsanity.com/paper/2206.00106/) (2022)
- [Measuring Diversity of Game Scenarios](https://mlsanity.com/paper/2404.15192/) (2024)
- [Minimal Neural Network Models for Permutation Invariant Agents](https://mlsanity.com/paper/2205.07868/) (2022)
- [Using Multiple Generative Adversarial Networks to Build Better-Connected Levels for Mega Man](https://mlsanity.com/paper/2102.00337/) (2021)
- [Procedural Content Generation via Knowledge Transformation (PCG-KT)](https://mlsanity.com/paper/2305.00644/) (2023)
- [Toward Co-creative Dungeon Generation via Transfer Learning](https://mlsanity.com/paper/2107.12533/) (2021)
- [Audio-guided Album Cover Art Generation with Genetic Algorithms](https://mlsanity.com/paper/2207.07162/) (2022)
- [Controllable Game Level Generation: Assessing the Effect of Negative Examples in GAN Models](https://mlsanity.com/paper/2410.23108/) (2024)
- [Video Game Level Design as a Multi-Agent Reinforcement Learning Problem](https://mlsanity.com/paper/2510.04862/) (2025)
- [Intercategorical Label Interpolation for Emotional Face Generation with Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2204.12237/) (2022)
- [Collaborative Interactive Evolution of Art in the Latent Space of Deep Generative Models](https://mlsanity.com/paper/2403.19620/) (2024)
- [Improving Conditional Level Generation using Automated Validation in Match-3 Games](https://mlsanity.com/paper/2409.06349/) (2024)
- [Text-to-Level Diffusion Models With Various Text Encoders for Super Mario Bros](https://mlsanity.com/paper/2507.00184/) (2025)
- [Learning Local Constraints for Reinforcement-Learned Content Generators](https://mlsanity.com/paper/2605.13570/) (2026)

## Abstract (arXiv)

Generative Adversarial Networks (GANs) are an emerging form of indirect encoding. The GAN is trained to induce a latent space on training data, and a real-valued evolutionary algorithm can search that latent space. Such Latent Variable Evolution (LVE) has recently been applied to game levels. However, it is hard for objective scores to capture level features that are appealing to players. Therefore, this paper introduces a tool for interactive LVE of tile-based levels for games. The tool also allows for direct exploration of the latent dimensions, and allows users to play discovered levels. The tool works for a variety of GAN models trained for both Super Mario Bros. and The Legend of Zelda, and is easily generalizable to other games. A user study shows that both the evolution and latent space exploration features are appreciated, with a slight preference for direct exploration, but combining these features allows users to discover even better levels. User feedback also indicates how this system could eventually grow into a commercial design tool, with the addition of a few enhancements.