# The N-Tuple Bandit Evolutionary Algorithm for Game Agent Optimisation

arXiv: https://arxiv.org/abs/1802.05991 · HTML: https://mlsanity.com/paper/1802.05991/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2018.

## Built on by (most cited first, 9 of 9)

- [Evolving Evaluation Functions for Collectible Card Game AI](https://mlsanity.com/paper/2105.01115/) (2021)
- [Evolutionary Multi-Armed Bandits with Genetic Thompson Sampling](https://mlsanity.com/paper/2205.10113/) (2022)
- [Genetic multi-armed bandits: a reinforcement learning approach for discrete optimization via simulation](https://mlsanity.com/paper/2302.07695/) (2023)
- [Portfolio Search and Optimization for General Strategy Game-Playing](https://mlsanity.com/paper/2104.10429/) (2021)
- [Elastic Monte Carlo Tree Search with State Abstraction for Strategy Game Playing](https://mlsanity.com/paper/2205.15126/) (2022)
- [Flexible K Nearest Neighbors Classifier: Derivation and Application for Ion-mobility Spectrometry-based Indoor Localization](https://mlsanity.com/paper/2304.10151/) (2023)
- [Strategy Game-Playing with Size-Constrained State Abstraction](https://mlsanity.com/paper/2408.06202/) (2024)
- [JSON-Bag: A generic game trajectory representation](https://mlsanity.com/paper/2508.00712/) (2025)
- [Visualising Multiplayer Game Spaces](https://mlsanity.com/paper/2202.05773/) (2022)

## Abstract (arXiv)

This paper describes the N-Tuple Bandit Evolutionary Algorithm (NTBEA), an optimisation algorithm developed for noisy and expensive discrete (combinatorial) optimisation problems. The algorithm is applied to two game-based hyper-parameter optimisation problems. The N-Tuple system directly models the statistics, approximating the fitness and number of evaluations of each modelled combination of parameters. The model is simple, efficient and informative. Results show that the NTBEA significantly outperforms grid search and an estimation of distribution algorithm.