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

TL;DR

This study offers a comprehensive examination of the BRKGA metaheuristic and its various applications, shedding light on key areas for future research.

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.

Figures (9)

• Figure 1: Number of BRKGA articles published per year.
• Figure 2: Callon's diagram. Adapted from Callon1991:coword_analysis_diagram
• Figure 3: Collaboration network of the articles. The size of the nodes indicates a higher amount of articles, while the thickness of the edges indicates a higher amount of collaborations. Edges are only shown if the authors collaborated on more than two papers.
• Figure 4: Co-citation network of the articles. The thickness of the edges indicates a stronger co-citation relationship.
• Figure 5: Co-occurrence network for the keywords observed in the works.