Szczegóły publikacji
Opis bibliograficzny
Coding agents for automated metaheuristic design / Jan Iłowski, Marcin Małek, Wojciech ACHTELIK, Maciej SMOŁKA // W: GECCO'26 Companion [Dokument elektroniczny] : proceedings of the 2026 Genetic and Evolutionary Computation Conference : San Jose, Costa Rica, July 13–17, 2026 / eds. Leonardo Trujillo, Ting Hu. — Wersja do Windows. — Dane tekstowe. — New York : Association for Computing Machinery, cop. 2026. — e-ISBN: 979-8-4007-2488-6. — S. 7–8. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 8, Abstr. — Publikacja dostępna online od: 2026-08-13
Autorzy (4)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 170294 |
|---|---|
| Data dodania do BaDAP | 2026-10-08 |
| Tekst źródłowy | URL |
| DOI | 10.1145/3795101.3815571 |
| Rok publikacji | 2026 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Creative Commons | |
| Wydawca | Association for Computing Machinery (ACM) |
| Konferencja | Genetic and Evolutionary Computations 2026 |
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.
