  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.
