Automated Design of Metaheuristic Algorithms: A Survey
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866913238140059648 |
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| author | Zhao, Qi Duan, Qiqi Yan, Bai Cheng, Shi Shi, Yuhui |
| author_facet | Zhao, Qi Duan, Qiqi Yan, Bai Cheng, Shi Shi, Yuhui |
| contents | Metaheuristics have gained great success in academia and practice because their search logic can be applied to any problem with available solution representation, solution quality evaluation, and certain notions of locality. Manually designing metaheuristic algorithms for solving a target problem is criticized for being laborious, error-prone, and requiring intensive specialized knowledge. This gives rise to increasing interest in automated design of metaheuristic algorithms. With computing power to fully explore potential design choices, the automated design could reach and even surpass human-level design and could make high-performance algorithms accessible to a much wider range of researchers and practitioners. This paper presents a broad picture of automated design of metaheuristic algorithms, by conducting a survey on the common grounds and representative techniques in terms of design space, design strategies, performance evaluation strategies, and target problems in this field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_06532 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Automated Design of Metaheuristic Algorithms: A Survey Zhao, Qi Duan, Qiqi Yan, Bai Cheng, Shi Shi, Yuhui Neural and Evolutionary Computing Machine Learning Metaheuristics have gained great success in academia and practice because their search logic can be applied to any problem with available solution representation, solution quality evaluation, and certain notions of locality. Manually designing metaheuristic algorithms for solving a target problem is criticized for being laborious, error-prone, and requiring intensive specialized knowledge. This gives rise to increasing interest in automated design of metaheuristic algorithms. With computing power to fully explore potential design choices, the automated design could reach and even surpass human-level design and could make high-performance algorithms accessible to a much wider range of researchers and practitioners. This paper presents a broad picture of automated design of metaheuristic algorithms, by conducting a survey on the common grounds and representative techniques in terms of design space, design strategies, performance evaluation strategies, and target problems in this field. |
| title | Automated Design of Metaheuristic Algorithms: A Survey |
| topic | Neural and Evolutionary Computing Machine Learning |
| url | https://arxiv.org/abs/2303.06532 |