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

Abstract

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.

32 pages, 9 figures

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• The Journal Coverage of Web of Science, Scopus and Dimensions: A Comparative Analysis
• Kernel methods in machine learning
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• Biased Random-Key Genetic Algorithms: A Review

Cited by in corpus (1)

• Random-key genetic algorithms: Principles and applications