Optimizing Photonic Structures with Large Language Model Driven Algorithm Discovery
Genetic and Evolutionary Computation Conference Companion (GECCO Companion), pp. 2354–2362
Abstract
We study how large language models (LLMs) 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 configurations of Evolution 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. Such an approach is broadly applicable and can transform design processes across a wide range of scientific and engineering domains.
Authors 4
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Affiliation as printed
Leiden Institute of Advanced Computer Science, Leiden, Netherlands
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Affiliation as printed
Leiden Institute of Advanced Computer Science, Leiden, Netherlands
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Affiliation as printed
Leiden Institute of Advanced Computer Science, Leiden, Netherlands
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Affiliation as printed
Leiden Institute of Advanced Computer Science, Leiden, Netherlands
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