Early years of Biased Random-Key Genetic Algorithms: A systematic review
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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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 May 2024.
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.
- 2025Random-key genetic algorithms: Principles and applicationscited by 0
- 2025Constraint programming model and biased random-key genetic algorithm for the single-machine coupled task scheduling problem with exact delays to minimize the makespancited by 0
- 2026Robust Metaheuristics under Uncertainty for Berth Allocation and Quay Crane Assignment: A Reviewcited by 0
- 2026Robust Metaheuristics under Uncertainty for Berth Allocation and Quay Crane Assignment: A Review6 Jul 2026
- 2025Constraint programming model and biased random-key genetic algorithm for the single-machine coupled task scheduling problem with exact delays to minimize the makespan29 Dec 2025
- 2025Random-key genetic algorithms: Principles and applications2 Jun 2025