Early years of Biased Random-Key Genetic Algorithms: A systematic review
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912110140719104 |
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| author | Londe, Mariana A. Pessoa, Luciana S. Andrade, Cartlos E. Resende, Mauricio G. C. |
| author_facet | Londe, Mariana A. Pessoa, Luciana S. Andrade, Cartlos E. Resende, Mauricio G. C. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_01765 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Early years of Biased Random-Key Genetic Algorithms: A systematic review Londe, Mariana A. Pessoa, Luciana S. Andrade, Cartlos E. Resende, Mauricio G. C. Neural and Evolutionary Computing Optimization and Control 90 A.1; I.2; G.2; G.4 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. |
| title | Early years of Biased Random-Key Genetic Algorithms: A systematic review |
| topic | Neural and Evolutionary Computing Optimization and Control 90 A.1; I.2; G.2; G.4 |
| url | https://arxiv.org/abs/2405.01765 |