Early years of Biased Random-Key Genetic Algorithms: A systematic review
<|reference_start|>Early years of Biased Random-Key Genetic Algorithms: A systematic review: 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.<|reference_end|>
@article{londe2024early,
    title={Early years of Biased Random-Key Genetic Algorithms: A systematic review},
    author={Mariana A. Londe, Luciana S. Pessoa, Cartlos E. Andrade, Mauricio G.C.
  Resende},
    journal={J Glob Optim (2024)},
    year={2024},
    doi={10.1007/s10898-024-01446-5},
    archivePrefix={arXiv},
    eprint={2405.01765},
    primaryClass={cs.NE math.OC}
}