Leveraging Large Language Models for the generation of novel metaheuristic optimization algorithms
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F23%3A63570738" target="_blank" >RIV/70883521:28140/23:63570738 - isvavai.cz</a>
Result on the web
<a href="https://dl.acm.org/doi/10.1145/3583133.3596401" target="_blank" >https://dl.acm.org/doi/10.1145/3583133.3596401</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3583133.3596401" target="_blank" >10.1145/3583133.3596401</a>
Alternative languages
Result language
angličtina
Original language name
Leveraging Large Language Models for the generation of novel metaheuristic optimization algorithms
Original language description
In this paper, we investigate the potential of using Large Language Models (LLMs) such as GPT-4 to generate novel hybrid swarm intelligence optimization algorithms. We use the LLM to identify and decompose six well-performing swarm algorithms for continuous optimization: Particle Swarm Optimization (PSO), Cuckoo Search (CS), Artificial Bee Colony (ABC), Grey Wolf Optimizer (GWO), Self-Organizing Migrating Algorithm (SOMA), and Whale Optimization Algorithm (WOA). We leverage GPT-4 to propose a hybrid algorithm that combines the strengths of these techniques for two distinct use-case scenarios. Our focus is on the process itself and various challenges that emerge during the use of GPT-4 to fulfill a series of set tasks. Furthermore, we discuss the potential impact of LLM-generated algorithms in the metaheuristics domain and explore future research directions. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Czech name
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Czech description
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Classification
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)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
GECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion
ISBN
979-840070120-7
ISSN
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e-ISSN
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Number of pages
9
Pages from-to
1812-1820
Publisher name
Association for Computing Machinery, Inc
Place of publication
New York
Event location
Lisbon
Event date
Jul 15, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001117972600294