Szczegóły publikacji
Opis bibliograficzny
EASE-ing into global optimization with LLMs : a competition entry on LLM-designed evolutionary algorithms at the Genetic and Evolutionary Computation Conference (GECCO) 2025 / Adam Viktorin, Tomas Kadavy, Jozef Kovac, Michal PLUHÁČEK, Roman Senkerin // W: GECCO'25 Companion [Dokument elektroniczny] : proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion : July 14–18, 2025, Málaga, Spain. — Wersja do Windows. — Dane tekstowe. — New York : Association for Computing Machinery, 2025. — e-ISBN: 979-8-4007-1464-1. — S. 7–8. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 8, Abstr. — Publikacja dostępna online od: 2025-08-11. — M. Pluháček - afiliacja: Center of Excellence in AI, Krakow
Autorzy (5)
- Viktorin Adam
- Kadavy Tomas
- Kovac Jozef
- AGHPluháček Michal
- Senkerik Roman
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 161859 |
|---|---|
| Data dodania do BaDAP | 2025-09-02 |
| Tekst źródłowy | URL |
| DOI | 10.1145/3712255.3735096 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Creative Commons | |
| Wydawca | Association for Computing Machinery (ACM) |
| Konferencja | Genetic and Evolutionary Computations 2025 |
Abstract
This paper presents an extended abstract describing an entry into the LLM for evolutionary algorithms tailored benchmarking competition using a GNBG-generated test suite. This study explores the generative design of optimization algorithms using large language models (LLMs) within the EASE modular framework, which supports iterative prompting and feedback-driven refinement. Across five successive generations, we observed a progressive transformation in algorithmic structure. The findings suggest opportunities for further research into the role of prompt design, feedback phrasing, and framework architecture in guiding the emergence of more task-adaptive, domain-specialized algorithmic behavior.
