AutoEP: LLMs-Driven Automation of Hyperparameter Evolution for Metaheuristic Algorithms
Zhenxing Xu, Yizhe Zhang, Weidong Bao, Hao Wang, Ming Chen, Haoran Ye, Wenzheng Jiang, Hui Yan, Ji Wang
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.
AutoEP uses LLM reasoning with real-time landscape analysis to dynamically control metaheuristic algorithms without training.
- Introduces AutoEP framework leveraging LLMs as zero-shot reasoning engines for algorithm configuration
- Synergizes Exploratory Landscape Analysis (ELA) module with multi-LLM reasoning chain for hyperparameter adaptation
- Grounds high-level reasoning in empirical search data, mitigating hallucination
- Demonstrates open-source Qwen3-30B matches GPT-4 performance on hyperparameter tuning
- Large language models
- Exploratory landscape analysis
- Zero-shot reasoning
- Metaheuristic algorithms
- Combinatorial optimization benchmarks
Authors did not state explicit limitations.
Authors did not state explicit future directions.
Author keywords
- LLMs
- Optimization
- Metaheuristic algorithm
- Automatic Algorithm Design
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