# Genetic multi-armed bandits: a reinforcement learning approach for discrete optimization via simulation

arXiv: https://arxiv.org/abs/2302.07695 · HTML: https://mlsanity.com/paper/2302.07695/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 15 Feb 2023.

## Builds on (most cited first, 6 of 6)

- [On the Complexity of Best Arm Identification in Multi-Armed Bandit Models](https://mlsanity.com/paper/1407.4443/) (2014)
- [Review on Ranking and Selection: A New Perspective](https://mlsanity.com/paper/2008.00249/) (2020)
- [Simulation optimization: A review of algorithms and applications](https://mlsanity.com/paper/1706.08591/) (2017)
- [The N-Tuple Bandit Evolutionary Algorithm for Game Agent Optimisation](https://mlsanity.com/paper/1802.05991/) (2018)
- [Bandit-Based Random Mutation Hill-Climbing](https://mlsanity.com/paper/1606.06041/) (2016)
- [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)

- [Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs](https://mlsanity.com/paper/2411.10906/) (2024)
- [Financial Decision Making using Reinforcement Learning with Dirichlet Priors and Quantum-Inspired Genetic Optimization](https://mlsanity.com/paper/2509.00095/) (2025)

## Abstract (arXiv)

This paper proposes a new algorithm, referred to as GMAB, that combines concepts from the reinforcement learning domain of multi-armed bandits and random search strategies from the domain of genetic algorithms to solve discrete stochastic optimization problems via simulation. In particular, the focus is on noisy large-scale problems, which often involve a multitude of dimensions as well as multiple local optima. Our aim is to combine the property of multi-armed bandits to cope with volatile simulation observations with the ability of genetic algorithms to handle high-dimensional solution spaces accompanied by an enormous number of feasible solutions. For this purpose, a multi-armed bandit framework serves as a foundation, where each observed simulation is incorporated into the memory of GMAB. Based on this memory, genetic operators guide the search, as they provide powerful tools for exploration as well as exploitation. The empirical results demonstrate that GMAB achieves superior performance compared to benchmark algorithms from the literature in a large variety of test problems. In all experiments, GMAB required considerably fewer simulations to achieve similar or (far) better solutions than those generated by existing methods. At the same time, GMAB's overhead with regard to the required runtime is extremely small due to the suggested tree-based implementation of its memory. Furthermore, we prove its convergence to the set of global optima as the simulation effort goes to infinity.