Metaheuristics and Large Language Models Join Forces: Toward an Integrated Optimization Approach
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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_ | 1866910822596345856 |
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| author | Sartori, Camilo Chacón Blum, Christian Bistaffa, Filippo Corominas, Guillem Rodríguez |
| author_facet | Sartori, Camilo Chacón Blum, Christian Bistaffa, Filippo Corominas, Guillem Rodríguez |
| contents | Since the rise of Large Language Models (LLMs) a couple of years ago, researchers in metaheuristics (MHs) have wondered how to use their power in a beneficial way within their algorithms. This paper introduces a novel approach that leverages LLMs as pattern recognition tools to improve MHs. The resulting hybrid method, tested in the context of a social network-based combinatorial optimization problem, outperforms existing state-of-the-art approaches that combine machine learning with MHs regarding the obtained solution quality. By carefully designing prompts, we demonstrate that the output obtained from LLMs can be used as problem knowledge, leading to improved results. Lastly, we acknowledge LLMs' potential drawbacks and limitations and consider it essential to examine them to advance this type of research further. Our method can be reproduced using a tool available at: https://github.com/camilochs/optipattern. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_18272 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Metaheuristics and Large Language Models Join Forces: Toward an Integrated Optimization Approach Sartori, Camilo Chacón Blum, Christian Bistaffa, Filippo Corominas, Guillem Rodríguez Artificial Intelligence 68T20 Since the rise of Large Language Models (LLMs) a couple of years ago, researchers in metaheuristics (MHs) have wondered how to use their power in a beneficial way within their algorithms. This paper introduces a novel approach that leverages LLMs as pattern recognition tools to improve MHs. The resulting hybrid method, tested in the context of a social network-based combinatorial optimization problem, outperforms existing state-of-the-art approaches that combine machine learning with MHs regarding the obtained solution quality. By carefully designing prompts, we demonstrate that the output obtained from LLMs can be used as problem knowledge, leading to improved results. Lastly, we acknowledge LLMs' potential drawbacks and limitations and consider it essential to examine them to advance this type of research further. Our method can be reproduced using a tool available at: https://github.com/camilochs/optipattern. |
| title | Metaheuristics and Large Language Models Join Forces: Toward an Integrated Optimization Approach |
| topic | Artificial Intelligence 68T20 |
| url | https://arxiv.org/abs/2405.18272 |