Paper ID: 2405.01765

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

Mariana A. Londe, Luciana S. Pessoa, Cartlos E. Andrade, Mauricio G. C. Resende

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.

Submitted: May 2, 2024