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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.23189 |
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| _version_ | 1866911516735832064 |
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| author | Xu, Zhenxing Zhang, Yizhe Bao, Weidong Wang, Hao Chen, Ming Ye, Haoran Jiang, Wenzheng Yan, Hui Wang, Ji |
| author_facet | Xu, Zhenxing Zhang, Yizhe Bao, Weidong Wang, Hao Chen, Ming Ye, Haoran Jiang, Wenzheng Yan, Hui Wang, Ji |
| contents | Dynamically configuring algorithm hyperparameters is a fundamental challenge in computational intelligence. While learning-based methods offer automation, they suffer from prohibitive sample complexity and poor generalization. We introduce AutoEP, a novel framework that bypasses training entirely by leveraging Large Language Models (LLMs) as zero-shot reasoning engines for algorithm control. AutoEP's core innovation lies in a tight synergy between two components: (1) an online Exploratory Landscape Analysis (ELA) module that provides real-time, quantitative feedback on the search dynamics, and (2) a multi-LLM reasoning chain that interprets this feedback to generate adaptive hyperparameter strategies. This approach grounds high-level reasoning in empirical data, mitigating hallucination. Evaluated on three distinct metaheuristics across diverse combinatorial optimization benchmarks, AutoEP consistently outperforms state-of-the-art tuners, including neural evolution and other LLM-based methods. Notably, our framework enables open-source models like Qwen3-30B to match the performance of GPT-4, demonstrating a powerful and accessible new paradigm for automated hyperparameter design. Our code is available at https://github.com/YiZheZhang12/AutoEP. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23189 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AutoEP: LLMs-Driven Automation of Hyperparameter Evolution for Metaheuristic Algorithms Xu, Zhenxing Zhang, Yizhe Bao, Weidong Wang, Hao Chen, Ming Ye, Haoran Jiang, Wenzheng Yan, Hui Wang, Ji Artificial Intelligence Dynamically configuring algorithm hyperparameters is a fundamental challenge in computational intelligence. While learning-based methods offer automation, they suffer from prohibitive sample complexity and poor generalization. We introduce AutoEP, a novel framework that bypasses training entirely by leveraging Large Language Models (LLMs) as zero-shot reasoning engines for algorithm control. AutoEP's core innovation lies in a tight synergy between two components: (1) an online Exploratory Landscape Analysis (ELA) module that provides real-time, quantitative feedback on the search dynamics, and (2) a multi-LLM reasoning chain that interprets this feedback to generate adaptive hyperparameter strategies. This approach grounds high-level reasoning in empirical data, mitigating hallucination. Evaluated on three distinct metaheuristics across diverse combinatorial optimization benchmarks, AutoEP consistently outperforms state-of-the-art tuners, including neural evolution and other LLM-based methods. Notably, our framework enables open-source models like Qwen3-30B to match the performance of GPT-4, demonstrating a powerful and accessible new paradigm for automated hyperparameter design. Our code is available at https://github.com/YiZheZhang12/AutoEP. |
| title | AutoEP: LLMs-Driven Automation of Hyperparameter Evolution for Metaheuristic Algorithms |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2509.23189 |