Leveraging Large Language Models for the generation of novel metaheuristic optimization algorithms

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Leveraging Large Language Models for the generation of novel metaheuristic optimization algorithms

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Leveraging Large Language Models for the generation of novel metaheuristic optimization algorithms

Popis výsledku anglicky

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.

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)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

GECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion

ISBN

979-840070120-7

ISSN

—

e-ISSN

—

Počet stran výsledku

9

Strana od-do

1812-1820

Název nakladatele

Association for Computing Machinery, Inc

Místo vydání

New York

Místo konání akce

Lisbon

Datum konání akce

15. 7. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001117972600294