# Early years of Biased Random-Key Genetic Algorithms: A systematic review

arXiv: https://arxiv.org/abs/2405.01765 · HTML: https://mlsanity.com/paper/2405.01765/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2024.

## Built on by (most cited first, 3 of 3)

- [Random-key genetic algorithms: Principles and applications](https://mlsanity.com/paper/2506.02120/) (2025)
- [Constraint programming model and biased random-key genetic algorithm for the single-machine coupled task scheduling problem with exact delays to minimize the makespan](https://mlsanity.com/paper/2512.23150/) (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 presents a systematic literature review and bibliometric analysis focusing on Biased Random-Key Genetic Algorithms (BRKGA). BRKGA is a metaheuristic framework that uses random-key-based chromosomes with biased, uniform, and elitist mating strategies alongside a genetic algorithm. This review encompasses around~250 papers, covering a diverse array of applications ranging from classical combinatorial optimization problems to real-world industrial scenarios, and even non-traditional applications like hyperparameter tuning in machine learning and scenario generation for two-stage problems. In summary, this study offers a comprehensive examination of the BRKGA metaheuristic and its various applications, shedding light on key areas for future research.