Szczegóły publikacji
Opis bibliograficzny
Claude and Gemini design metaheuristics: a cooperative multi-LLM approach / Michal PLUHÁČEK, Paweł KOLENDO, Krzysztof TYLKA-SULEJA, Rohit SALGOTRA // 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. 13–14. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 14, Abstr. — Publikacja dostępna online od: 2026-08-13
Autorzy (4)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 170000 |
|---|---|
| Data dodania do BaDAP | 2026-09-29 |
| Tekst źródłowy | URL |
| DOI | 10.1145/3795101.3815566 |
| 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
In this entry to the GECCO 2026 Competition on LLM-Designed Metaheuristics, three large language models (GPT-5.4 Thinking, Gemini 3.1 Pro, Claude Opus 4.6) were independently prompted to design a metaheuristic for the GNBG benchmark (24 problems, hence the /24 scoring scale). Each reached a per-model ceiling (GPT 19.82, Claude 21.08, Gemini 23.29) and could not improve further on its own. Handing the best design (Gemini’s) to the second-best model (Claude) for refinement produced, within four iterations, EO-BIPOP-CMA-CDR, which ranks first in the full 24-algorithm field at 23.30/24 and outperforms the source design head-to-head by three score points. A single cross-model handoff yielded more progress than five further self-refinement iterations by the source model. We argue that model diversity is a usable, low-cost resource for LLM-driven algorithm design.
