The advent of Large Language Models (LLMs) has opened new frontiers in automated algorithm design, giving rise to numerous powerful methods. However, these approaches retain critical limitations: they require extensive evaluation of the target problem to guide the search process, making them impractical for real-world optimization tasks, where each evaluation consumes substantial computational resources. This research proposes an innovative and efficient framework that decouples algorithm discovery from high-cost evaluation. Our core innovation lies in combining a Genetic Programming (GP) function generator with an LLM-driven evolutionary algorithm designer. The evolutionary direction of the GP-based function generator is guided by the similarity between the landscape characteristics of generated proxy functions and those of real-world problems, ensuring that algorithms discovered via proxy functions exhibit comparable performance on real-world problems. Our method enables deep exploration of the algorithmic space before final validation while avoiding costly real-world evaluations. We validated the framework's efficacy across multiple real-world problems, demonstrating its ability to discover high-performance algorithms while substantially reducing expensive evaluations. This approach shows a path to apply LLM-based automated algorithm design to computationally intensive real-world optimization challenges.

翻译：大型语言模型（LLM）的出现为自动化算法设计开辟了新前沿，催生了众多强大方法。然而，这些方法仍存在关键局限：它们需要对目标问题进行大量评估以指导搜索过程，这在现实世界优化任务中并不实用，因为每次评估都会消耗大量计算资源。本研究提出了一种创新且高效的框架，将算法发现与高成本评估解耦。我们的核心创新在于将遗传规划（GP）函数生成器与LLM驱动的进化算法设计器相结合。基于GP的函数生成器的进化方向由生成代理函数的特征景观与现实问题特征景观之间的相似性引导，确保通过代理函数发现的算法在现实问题上表现出可比的性能。我们的方法能够在最终验证前深入探索算法空间，同时避免昂贵的现实世界评估。我们在多个现实问题上验证了该框架的有效性，证明了其发现高性能算法的能力，同时显著减少了昂贵的评估次数。这一方法为将基于LLM的自动化算法设计应用于计算密集的现实世界优化挑战指明了路径。