Szczegóły publikacji
Opis bibliograficzny
LLM-guided discovery of complementary metaheuristic operators with adaptive composition on the GNBG benchmark / Paweł KOLENDO, Michal PLUHÁČEK // 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. 11–12. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 12, Abstr. — Publikacja dostępna online od: 2026-08-13
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 170293 |
|---|---|
| Data dodania do BaDAP | 2026-10-01 |
| Tekst źródłowy | URL |
| DOI | 10.1145/3795101.3815567 |
| 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
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.
