用类别驱动提升大模型自动算法设计的稳定性与多样性
CDEoH: Category-Driven Automatic Algorithm Design With Large Language Models
- 引入算法类别管理机制，平衡性能与多样性
- 在多尺度组合优化问题上显著提升进化稳定性
- 适合研究自动化算法生成与优化的学者
随着大语言模型（LLMs）的快速发展，基于LLM的启发式搜索方法在自动化算法生成方面展现出强大能力。然而，其进化过程常面临不稳定和过早收敛的问题。现有方法主要通过提示工程或联合演化思维与代码来解决，却忽视了算法类别多样性对维持进化稳定性的关键作用。为此，我们提出基于大语言模型的类别驱动自动算法设计方法（CDEoH），显式建模算法类别，并在种群管理中协同平衡性能与类别多样性，实现多个算法范式的并行探索。在多种尺度的典型组合优化问题上的大量实验表明，CDEoH有效缓解了向单一进化方向收敛的问题，显著提升了进化稳定性，并在任务与尺度上均实现一致更优的平均性能。
原文摘要 · Abstract (English)
With the rapid advancement of large language models (LLMs), LLM-based heuristic search methods have demonstrated strong capabilities in automated algorithm generation. However, their evolutionary processes often suffer from instability and premature convergence. Existing approaches mainly address this issue through prompt engineering or by jointly evolving thought and code, while largely overlooking the critical role of algorithmic category diversity in maintaining evolutionary stability. To this end, we propose Category Driven Automatic Algorithm Design with Large Language Models (CDEoH), which explicitly models algorithm categories and jointly balances performance and category diversity in population management, enabling parallel exploration across multiple algorithmic paradigms. Extensive experiments on representative combinatorial optimization problems across multiple scales demonstrate that CDEoH effectively mitigates convergence toward a single evolutionary direction, significantly enhancing evolutionary stability and achieving consistently superior average performance across tasks and scales.
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