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.
@article{arxiv.2405.01765,
title = {Early years of Biased Random-Key Genetic Algorithms: A systematic review},
author = {Mariana A. Londe and Luciana S. Pessoa and Cartlos E. Andrade and Mauricio G. C. Resende},
journal= {arXiv preprint arXiv:2405.01765},
year = {2024}
}