Optimization can be found in many real-life applications. Designing an effective algorithm for a specific optimization problem typically requires a tedious amount of effort from human experts with domain knowledge and algorithm design skills. In this paper, we propose a novel approach called Algorithm Evolution using Large Language Model (AEL). It utilizes a large language model (LLM) to automatically generate optimization algorithms via an evolutionary framework. AEL does algorithm-level evolution without model training. Human effort and requirements for domain knowledge can be significantly reduced. We take constructive methods for the salesman traveling problem as a test example, we show that the constructive algorithm obtained by AEL outperforms simple hand-crafted and LLM-generated heuristics. Compared with other domain deep learning model-based algorithms, these methods exhibit excellent scalability across different problem sizes. AEL is also very different from previous attempts that utilize LLMs as search operators in algorithms.

翻译：优化存在于许多实际应用中。为特定优化问题设计高效算法通常需要具备领域知识和算法设计技能的人类专家投入大量繁琐的工作。本文提出了一种名为"基于大语言模型的算法进化"（AEL）的新方法。该方法利用大语言模型（LLM）通过进化框架自动生成优化算法。AEL在无需模型训练的情况下实现算法层面的进化，可显著减少人类投入及对领域知识的需求。以旅行商问题的构造性方法为例，实验表明AEL获得的构造算法优于简单手工设计及LLM生成的启发式方法。与其他基于领域深度学习模型的算法相比，该方法在不同问题规模上展现出优异的可扩展性。此外，AEL与以往将LLM作为算法中搜索算子的尝试存在本质区别。