A critical examination of large language model capabilities in iteratively refining differential evolution algorithm

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
A critical examination of large language model capabilities in iteratively refining differential evolution algorithm
Original language description
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.
Czech name
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Czech description
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Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/GF21-45465L" target="_blank" >GF21-45465L: Metaheuristic-based parametric optimization of time-delay models and control systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
GECCO 2024 Companion - Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion
ISBN
979-8-4007-0495-6
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
"1855 "- 1862
Publisher name
Association for Computing Machinery, Inc
Place of publication
New York
Event location
Melbourne
Event date
Jul 14, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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