用大模型提升进化算法搜索效率，解决复杂问题求解难题
Evolutionary thoughts: integration of large language models and evolutionary algorithms
- 结合大模型与进化算法，实现高效精准的解空间探索
- 实验证明该方法能生成更优解，显著提升求解质量
- 适合需要复杂优化的科研与工程场景
大型语言模型（LLMs）在理解和生成自然语言与代码方面展现出强大能力，但其推理易产生幻觉，在复杂新场景中常困于部分或错误解。而进化算法（EAs）擅长探索广阔复杂的搜索空间，在传统优化方法失效时表现优异。然而，面对复杂问题，EAs需评估大量个体，计算开销巨大。为此，我们提出一种高效的评估框架，兼容现有基础定义，确保生成有效个体。利用LLMs，我们设计了一种增强型进化搜索策略，使对庞大解空间的探索更具针对性。实验表明，该方法能有效生成更优候选解，显著提升求解性能。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have unveiled remarkable capabilities in understanding and generating both natural language and code, but LLM reasoning is prone to hallucination and struggle with complex, novel scenarios, often getting stuck on partial or incorrect solutions. However, the inherent ability of Evolutionary Algorithms (EAs) to explore extensive and complex search spaces makes them particularly effective in scenarios where traditional optimization methodologies may falter. However, EAs explore a vast search space when applied to complex problems. To address the computational bottleneck of evaluating large populations, particularly crucial for complex evolutionary tasks, we introduce a highly efficient evaluation framework. This implementation maintains compatibility with existing primitive definitions, ensuring the generation of valid individuals. Using LLMs, we propose an enhanced evolutionary search strategy that enables a more focused exploration of expansive solution spaces. LLMs facilitate the generation of superior candidate solutions, as evidenced by empirical results demonstrating their efficacy in producing improved outcomes.
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