Using LLM for automatic evolvement of metaheuristics from swarm algorithm SOMA

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F24%3A63587659" target="_blank" >RIV/70883521:28140/24:63587659 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1145/3638530.3664181" target="_blank" >http://dx.doi.org/10.1145/3638530.3664181</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3638530.3664181" target="_blank" >10.1145/3638530.3664181</a>

Result language
angličtina
Original language name
Using LLM for automatic evolvement of metaheuristics from swarm algorithm SOMA
Original language description
This study investigates the use of the GPT-4 Turbo, a large language model, to enhance the Self-Organizing Migrating Algorithm (SOMA), specifically its All to All variant (SOMA-ATA). Utilizing the model's extensive context capacity for iterative prompting without feedback, we sought to autonomously generate superior algorithmic versions. Contrary to our initial hypothesis, the improvements did not progress linearly. Nevertheless, one iteration stood out significantly, consistently outperforming the baseline across various pairwise comparisons and showing a robust performance profile. This iteration's structure deviated substantially from traditional SOMA principles, underscoring the potential of large language models to create distinctive and effective algorithmic strategies. The results affirm the methodology's ability to produce high-performing algorithms without expert intervention, setting the stage for future research to integrate feedback mechanisms and conduct detailed code analyses to understand further the modifications made by the Large Language Models.
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
—
Number of pages
5
Pages from-to
2018-2022
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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