Biased random-key genetic algorithms: A review

This paper is a comprehensive literature review of Biased Random-Key Genetic Algorithms (BRKGA). BRKGA is a metaheuristic that employs random-key-based chromosomes with biased, uniform, and elitist mating strategies in a genetic algorithm framework. The review encompasses over 150 papers with a wide...

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Published inEuropean journal of operational research Vol. 321; no. 1; pp. 1 - 22
Main Authors Londe, Mariana A., Pessoa, Luciana S., Andrade, Carlos E., Resende, Mauricio G.C.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 16.02.2025
Subjects
Applications
Biased random-key genetic algorithms
Literature review
Metaheuristics
Literature review
Applications
Metaheuristics
Biased random-key genetic algorithms
Online AccessGet full text
ISSN0377-2217
DOI10.1016/j.ejor.2024.03.030

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Abstract This paper is a comprehensive literature review of Biased Random-Key Genetic Algorithms (BRKGA). BRKGA is a metaheuristic that employs random-key-based chromosomes with biased, uniform, and elitist mating strategies in a genetic algorithm framework. The review encompasses over 150 papers with a wide range of applications, including classical combinatorial optimization problems, real-world industrial use cases, and non-orthodox applications such as neural network hyperparameter tuning in machine learning. Scheduling is by far the most prevalent application area in this review, followed by network design and location problems. The most frequent hybridization method employed is local search, and new features aim to increase population diversity. We also detail challenges and future directions for this method. Overall, this survey provides a comprehensive overview of the BRKGA metaheuristic and its applications and highlights important areas for future research. •Reviews 150+ BRKGA papers on optimization of complex problems.•Scheduling, network design and location are the most prevalent problems.•Hybridization including Local Search boosts BRKGA performance.•New features focus on increasing population diversity and escaping local optima.•Details challenges and directions for growth of this method.
AbstractList This paper is a comprehensive literature review of Biased Random-Key Genetic Algorithms (BRKGA). BRKGA is a metaheuristic that employs random-key-based chromosomes with biased, uniform, and elitist mating strategies in a genetic algorithm framework. The review encompasses over 150 papers with a wide range of applications, including classical combinatorial optimization problems, real-world industrial use cases, and non-orthodox applications such as neural network hyperparameter tuning in machine learning. Scheduling is by far the most prevalent application area in this review, followed by network design and location problems. The most frequent hybridization method employed is local search, and new features aim to increase population diversity. We also detail challenges and future directions for this method. Overall, this survey provides a comprehensive overview of the BRKGA metaheuristic and its applications and highlights important areas for future research. •Reviews 150+ BRKGA papers on optimization of complex problems.•Scheduling, network design and location are the most prevalent problems.•Hybridization including Local Search boosts BRKGA performance.•New features focus on increasing population diversity and escaping local optima.•Details challenges and directions for growth of this method.
Author Pessoa, Luciana S.
Andrade, Carlos E.
Londe, Mariana A.
Resende, Mauricio G.C.
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  surname: Londe
  fullname: Londe, Mariana A.
  email: mlonde@aluno.puc-rio.br
  organization: Department of Industrial Engineering, PUC-Rio, Rua Marquês de São Vicente, 225, Gávea 22453-900 Rio de Janeiro, RJ, Brazil
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  surname: Resende
  fullname: Resende, Mauricio G.C.
  email: mgcr@uw.edu
  organization: Industrial & Systems Engineering, University of Washington, 3900 E Stevens Way NE, Seattle, WA 98195, USA
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Biased random-key genetic algorithms
Literature review
Metaheuristics
Title Biased random-key genetic algorithms: A review
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