# Neuroevolution in Games: State of the Art and Open Challenges

arXiv: https://arxiv.org/abs/1410.7326 · HTML: https://mlsanity.com/paper/1410.7326/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2014.

## Built on by (most cited first, 15 of 15)

- [Symbolic Regression via Neural-Guided Genetic Programming Population Seeding](https://mlsanity.com/paper/2111.00053/) (2021)
- [Evolutionary Reinforcement Learning: A Survey](https://mlsanity.com/paper/2303.04150/) (2023)
- [Derivative-Free Reinforcement Learning: A Review](https://mlsanity.com/paper/2102.05710/) (2021)
- [Player-AI Interaction: What Neural Network Games Reveal About AI as Play](https://mlsanity.com/paper/2101.06220/) (2021)
- [Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL](https://mlsanity.com/paper/2106.15883/) (2021)
- [EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2311.07485/) (2023)
- [Evolution Guided Generative Flow Networks](https://mlsanity.com/paper/2402.02186/) (2024)
- [Learning Robust Policies for Generalized Debris Capture with an Automated Tether-Net System](https://mlsanity.com/paper/2201.04180/) (2022)
- [Multitask Neuroevolution for Reinforcement Learning with Long and Short Episodes](https://mlsanity.com/paper/2203.10844/) (2022)
- [EvolvingBehavior: Towards Co-Creative Evolution of Behavior Trees for Game NPCs](https://mlsanity.com/paper/2209.01020/) (2022)
- [Hybrid Self-Attention NEAT: A novel evolutionary approach to improve the NEAT algorithm](https://mlsanity.com/paper/2112.03670/) (2021)
- [Learning Dark Souls Combat Through Pixel Input With Neuroevolution](https://mlsanity.com/paper/2507.03793/) (2025)
- [Addressing Tactic Volatility in Self-Adaptive Systems Using Evolved Recurrent Neural Networks and Uncertainty Reduction Tactics](https://mlsanity.com/paper/2204.10308/) (2022)
- [Evolutionary Machine Learning and Games](https://mlsanity.com/paper/2311.16172/) (2023)
- [Personality Requires Struggle: Three Regimes of the Baldwin Effect in Neuroevolved Chess Agents](https://mlsanity.com/paper/2604.03565/) (2026)

## Abstract (arXiv)

This paper surveys research on applying neuroevolution (NE) to games. In neuroevolution, artificial neural networks are trained through evolutionary algorithms, taking inspiration from the way biological brains evolved. We analyse the application of NE in games along five different axes, which are the role NE is chosen to play in a game, the different types of neural networks used, the way these networks are evolved, how the fitness is determined and what type of input the network receives. The article also highlights important open research challenges in the field.