Leveraging Symbolic Regression for Heuristic Design in the Traveling Thief Problem
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866909175762649088 |
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| author | Ni, Andrew Spector, Lee |
| author_facet | Ni, Andrew Spector, Lee |
| contents | The Traveling Thief Problem is an NP-hard combination of the well known traveling salesman and knapsack packing problems. In this paper, we use symbolic regression to learn useful features of near-optimal packing plans, which we then use to design efficient metaheuristic genetic algorithms for the traveling thief algorithm. By using symbolic regression again to initialize the metaheuristic GA with near-optimal individuals, we are able to design a fast, interpretable, and effective packing initialization scheme. Comparisons against previous initialization schemes validates our algorithm design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_12750 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Leveraging Symbolic Regression for Heuristic Design in the Traveling Thief Problem Ni, Andrew Spector, Lee Neural and Evolutionary Computing The Traveling Thief Problem is an NP-hard combination of the well known traveling salesman and knapsack packing problems. In this paper, we use symbolic regression to learn useful features of near-optimal packing plans, which we then use to design efficient metaheuristic genetic algorithms for the traveling thief algorithm. By using symbolic regression again to initialize the metaheuristic GA with near-optimal individuals, we are able to design a fast, interpretable, and effective packing initialization scheme. Comparisons against previous initialization schemes validates our algorithm design. |
| title | Leveraging Symbolic Regression for Heuristic Design in the Traveling Thief Problem |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2404.12750 |