A critical examination of large language model capabilities in iteratively refining differential evolution algorithm
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F24%3A63587658" target="_blank" >RIV/70883521:28140/24:63587658 - isvavai.cz</a>
Výsledek na webu
<a href="http://dx.doi.org/10.1145/3638530.3664179" target="_blank" >http://dx.doi.org/10.1145/3638530.3664179</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3638530.3664179" target="_blank" >10.1145/3638530.3664179</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
A critical examination of large language model capabilities in iteratively refining differential evolution algorithm
Popis výsledku v původním jazyce
In this study, we investigate the applicability, challenges, and effectiveness of the advanced large language model GPT 4 Turbo in enhancing the selected metaheuristic algorithm, which is Differential Evolution. Our research primarily examines whether iterative, repetitive prompting could lead to progressive improvements in algorithm performance. We also explore the potential of developing enhanced algorithms through this process that markedly surpass the established baseline in terms of performance. In addition, the impact of the model's temperature parameter on these improvements is evaluated. As part of our diverse testing approach, we conduct an experiment where the best-performing algorithm from the initial phase is used as a new baseline. This step is to determine if further refinement via GPT 4 Turbo can achieve even better algorithmic efficiency. Finally, we have performed the benchmarking comparison of selected enhanced variants against the top three algorithms from the CEC 2022 competition.
Název v anglickém jazyce
A critical examination of large language model capabilities in iteratively refining differential evolution algorithm
Popis výsledku anglicky
In this study, we investigate the applicability, challenges, and effectiveness of the advanced large language model GPT 4 Turbo in enhancing the selected metaheuristic algorithm, which is Differential Evolution. Our research primarily examines whether iterative, repetitive prompting could lead to progressive improvements in algorithm performance. We also explore the potential of developing enhanced algorithms through this process that markedly surpass the established baseline in terms of performance. In addition, the impact of the model's temperature parameter on these improvements is evaluated. As part of our diverse testing approach, we conduct an experiment where the best-performing algorithm from the initial phase is used as a new baseline. This step is to determine if further refinement via GPT 4 Turbo can achieve even better algorithmic efficiency. Finally, we have performed the benchmarking comparison of selected enhanced variants against the top three algorithms from the CEC 2022 competition.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
<a href="/cs/project/GF21-45465L" target="_blank" >GF21-45465L: Metaheuristicky založená parametrická optimalizace modelů a řídicích systémů s dopravním zpožděním</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2024
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
GECCO 2024 Companion - Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion
ISBN
979-8-4007-0495-6
ISSN
—
e-ISSN
—
Počet stran výsledku
8
Strana od-do
"1855 "- 1862
Název nakladatele
Association for Computing Machinery, Inc
Místo vydání
New York
Místo konání akce
Melbourne
Datum konání akce
14. 7. 2024
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
—