Coding agents for automated metaheuristic design
TL;DR
The results suggest that recent progress in language models and tool use may already be sufficient to support practical automated metaheuristic design, and that recent progress in language models and tool use may already be sufficient to support practical automated metaheuristic design.
Abstract
This paper presents our submission to the competition on LLM-designed evolutionary algorithms for the GNBG benchmark suite. We study a simple alternative to recent automated algorithm-design frameworks: instead of placing the language model inside a specialized evolutionary loop, we treat optimizer construction as an ordinary coding task. In our setup, a general-purpose coding agent iteratively modifies and evaluates a candidate optimizer inside a small repository that provides a fixed interface, benchmarking harness, and lightweight experiment log. The main question is whether such an agent, without any bespoke search controller, can still discover competitive metaheuristics. Our results indicate that the answer is positive: the resulting optimizer performs well on a substantial subset of GNBG II. These findings suggest that recent progress in language models and tool use may already be sufficient to support practical automated metaheuristic design.