# Evolutionary Multi-Armed Bandits with Genetic Thompson Sampling

arXiv: https://arxiv.org/abs/2205.10113 · HTML: https://mlsanity.com/paper/2205.10113/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 26 Apr 2022.

## Builds on (most cited first, 12 of 12)

- [Cooperative Multi-Agent Bandits with Heavy Tails](https://mlsanity.com/paper/2008.06244/) (2020)
- [Kernel Methods for Cooperative Multi-Agent Contextual Bandits](https://mlsanity.com/paper/2008.06220/) (2020)
- [Multi-armed Bandit Problem with Known Trend](https://mlsanity.com/paper/1508.07091/) (2015)
- [Unified Models of Human Behavioral Agents in Bandits, Contextual Bandits and RL](https://mlsanity.com/paper/2005.04544/) (2020)
- [The N-Tuple Bandit Evolutionary Algorithm for Game Agent Optimisation](https://mlsanity.com/paper/1802.05991/) (2018)
- [Speaker Diarization as a Fully Online Learning Problem in MiniVox](https://mlsanity.com/paper/2006.04376/) (2020)
- [Online Learning in Iterated Prisoner's Dilemma to Mimic Human Behavior](https://mlsanity.com/paper/2006.06580/) (2020)
- [Online Semi-Supervised Learning in Contextual Bandits with Episodic Reward](https://mlsanity.com/paper/2009.08457/) (2020)
- [Optimal Epidemic Control as a Contextual Combinatorial Bandit with Budget](https://mlsanity.com/paper/2106.15808/) (2021)
- [Bandit-Based Random Mutation Hill-Climbing](https://mlsanity.com/paper/1606.06041/) (2016)
- [The N-Tuple Bandit Evolutionary Algorithm for Automatic Game Improvement](https://mlsanity.com/paper/1705.01080/) (2017)
- [Enhancing Evolutionary Conversion Rate Optimization via Multi-armed Bandit Algorithms](https://mlsanity.com/paper/1803.03737/) (2018)

## Built on by (most cited first, 2 of 2)

- [OverThink: Slowdown Attacks on Reasoning LLMs](https://mlsanity.com/paper/2502.02542/) (2025)
- [Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlook](https://mlsanity.com/paper/2210.13623/) (2022)

## Abstract (arXiv)

As two popular schools of machine learning, online learning and evolutionary computations have become two important driving forces behind real-world decision making engines for applications in biomedicine, economics, and engineering fields. Although there are prior work that utilizes bandits to improve evolutionary algorithms' optimization process, it remains a field of blank on how evolutionary approach can help improve the sequential decision making tasks of online learning agents such as the multi-armed bandits. In this work, we propose the Genetic Thompson Sampling, a bandit algorithm that keeps a population of agents and update them with genetic principles such as elite selection, crossover and mutations. Empirical results in multi-armed bandit simulation environments and a practical epidemic control problem suggest that by incorporating the genetic algorithm into the bandit algorithm, our method significantly outperforms the baselines in nonstationary settings. Lastly, we introduce EvoBandit, a web-based interactive visualization to guide the readers through the entire learning process and perform lightweight evaluations on the fly. We hope to engage researchers into this growing field of research with this investigation.