Biased Random-Key Genetic Algorithms: A Review
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866912109951975424 |
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| author | Londe, Mariana A. Pessoa, Luciana S. Andrade, Carlos E. Resende, Mauricio G. C. |
| author_facet | Londe, Mariana A. Pessoa, Luciana S. Andrade, Carlos E. Resende, Mauricio G. C. |
| contents | 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. Overall, this survey provides a comprehensive overview of the BRKGA metaheuristic and its applications and highlights important areas for future research. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2312_00961 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Biased Random-Key Genetic Algorithms: A Review Londe, Mariana A. Pessoa, Luciana S. Andrade, Carlos E. Resende, Mauricio G. C. Neural and Evolutionary Computing 90, 68 F.2.2; G.2.3 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. Overall, this survey provides a comprehensive overview of the BRKGA metaheuristic and its applications and highlights important areas for future research. |
| title | Biased Random-Key Genetic Algorithms: A Review |
| topic | Neural and Evolutionary Computing 90, 68 F.2.2; G.2.3 |
| url | https://arxiv.org/abs/2312.00961 |