We study how large language models can be used in combination with evolutionary computation techniques to automatically discover optimization algorithms for the design of photonic structures. Building on the Large Language Model Evolutionary Algorithm (LLaMEA) framework, we introduce structured prompt engineering tailored to multilayer photonic problems such as Bragg mirror, ellipsometry inverse analysis, and solar cell antireflection coatings. We systematically explore multiple evolutionary strategies, including (1+1), (1+5), (2+10), and others, to balance exploration and exploitation. Our experiments show that LLM-generated algorithms, generated using small-scale problem instances, can match or surpass established methods like quasi-oppositional differential evolution on large-scale realistic real-world problem instances. Notably, LLaMEA's self-debugging mutation loop, augmented by automatically extracted problem-specific insights, achieves strong anytime performance and reliable convergence across diverse problem scales. This work demonstrates the feasibility of domain-focused LLM prompts and evolutionary approaches in solving optical design tasks, paving the way for rapid, automated photonic inverse design.

翻译：本研究探讨如何将大语言模型与进化计算技术相结合，以自动发现用于光子结构设计的优化算法。基于大语言模型进化算法框架，我们引入了针对多层光子问题的结构化提示工程，包括布拉格反射镜、椭偏仪逆分析及太阳能电池减反射涂层等应用。我们系统探索了多种进化策略，如(1+1)、(1+5)、(2+10)等，以平衡探索与利用能力。实验表明，通过小规模问题实例生成的LLM算法，在大规模实际应用场景中能够匹配甚至超越准反向差分进化等传统方法。值得注意的是，LLaMEA框架通过自动提取问题特定洞察增强的自调试变异循环，在不同规模问题上均展现出优异的实时性能与可靠收敛性。本工作验证了领域聚焦的LLM提示与进化方法在解决光学设计任务中的可行性，为快速自动化的光子逆向设计开辟了新途径。