LLM-Guided Discovery of Complementary Metaheuristic Operators with Adaptive Composition on the GNBG Benchmark

Abstract

We propose a framework for automated discovery and composition of metaheuristic operators using large language models (LLMs). The method begins with generation of candidate algorithms, followed by LLM-guided selection of impactful operators, iterative construction of specialized operator variants, and final synthesis of an adaptive optimizer. The approach emphasizes task coverage rather than single-metric optimization, producing complementary operators specialized for different problem characteristics. These operators are subsequently integrated into an adaptive algorithm via LLM-driven parameter control and selection mechanisms. This work is submitted as a competition entry for LLM-generated metaheuristics evaluated on the GNBG benchmark.