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

TL;DR

This systematic review analyzes the development and diverse applications of Biased Random-Key Genetic Algorithms (BRKGA), highlighting its evolution, key research areas, and potential future directions in optimization and machine learning.

Contribution

It provides the first comprehensive review of BRKGA, covering around 250 papers and diverse applications, and identifies future research opportunities.

Findings

BRKGA has been applied to a wide range of problems including combinatorial optimization and machine learning.

The review highlights the evolution and key research trends in BRKGA development.

Future research directions include expanding applications and improving algorithm efficiency.

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.