AutoEP: LLMs-Driven Automation of Hyperparameter Evolution for Metaheuristic Algorithms

• URL: http://arxiv.org/abs/2509.23189v1
• Date: Sat, 27 Sep 2025 08:45:21 GMT
• Title: AutoEP: LLMs-Driven Automation of Hyperparameter Evolution for Metaheuristic Algorithms
• Authors: Zhenxing Xu, Yizhe Zhang, Weidong Bao, Hao Wang, Ming Chen, Haoran Ye, Wenzheng Jiang, Hui Yan, Ji Wang,
• Abstract summary: We introduce AutoEP, a novel framework that bypasses training entirely by leveraging Large Language Models (LLMs) as zero-shot reasoning engines for algorithm control.<n>AutoEP consistently outperforms state-of-the-art tuning methods.
• Score: 22.971467368498452
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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://anonymous.4open.science/r/AutoEP-3E11

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