From Experience to Knowledge: Algorithm Design for Black-Box Optimization with Large Language Models

Large language models (LLMs) have shown promise in automated evolutionary algorithm (EA) design through their ability to generate algorithmic code and iteratively refine it based on performance feedback. However, many existing methods rely on iterative search to design algorithms for target problems, requiring repeated LLM calls and candidate evaluations. Moreover, design experience obtained on a target problem may not provide guidance for algorithm design on subsequent problems. To address these limitations, we propose FETK, a three-stage framework that distills problem algorithm design experience into knowledge, enabling an EA architecture to be generated for each subsequent problem with a single LLM call. First, the Experience Collection module collects problem design experience by searching EA architectures on training problems and recording landscape representations, architectural modifications, and performance outcomes. Second, the Knowledge Learning module learns design knowledge by distilling experience across training problems into natural-language guidance and evaluating candidate knowledge updates on validation problems before retaining them. Finally, the Knowledge Reuse module combines the learned knowledge with the landscape representation of each test problem to generate an EA architecture with a single LLM call. Experiments on BBOB and MA-BBOB problems demonstrate competitive performance compared with manually designed EA methods, learning-based design EA methods, and LLM-based design EA methods. https://anonymous.4open.science/r/fetk-DE22