# Biased Random-Key Genetic Algorithms: A Review

arXiv: https://arxiv.org/abs/2312.00961 · HTML: https://mlsanity.com/paper/2312.00961/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2023.

## Built on by (most cited first, 6 of 6)

- [Large Language Models for Combinatorial Optimization: A Systematic Review](https://mlsanity.com/paper/2507.03637/) (2025)
- [Model Agnostic Hybrid Sharding For Heterogeneous Distributed Inference](https://mlsanity.com/paper/2407.19775/) (2024)
- [A Random-Key Optimizer for Combinatorial Optimization](https://mlsanity.com/paper/2411.04293/) (2024)
- [Random-Key Algorithms for Optimizing Integrated Operating Room Scheduling](https://mlsanity.com/paper/2501.10243/) (2025)
- [Random-key genetic algorithms: Principles and applications](https://mlsanity.com/paper/2506.02120/) (2025)
- [Robust Metaheuristics under Uncertainty for Berth Allocation and Quay Crane Assignment: A Review](https://mlsanity.com/paper/2608.19214/) (2026)

## Abstract (arXiv)

This paper is a comprehensive literature review of Biased Random-Key Genetic Algorithms (BRKGA). BRKGA is a metaheuristic that employs random-key-based chromosomes with biased, uniform, and elitist mating strategies in a genetic algorithm framework. The review encompasses over 150 papers with a wide range of applications, including classical combinatorial optimization problems, real-world industrial use cases, and non-orthodox applications such as neural network hyperparameter tuning in machine learning. Scheduling is by far the most prevalent application area in this review, followed by network design and location problems. The most frequent hybridization method employed is local search, and new features aim to increase population diversity. Overall, this survey provides a comprehensive overview of the BRKGA metaheuristic and its applications and highlights important areas for future research.