Investigating the Potential of AI-Driven Innovations for Enhancing Differential Evolution in Optimization Tasks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F23%3A63573938" target="_blank" >RIV/70883521:28140/23:63573938 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10394233" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10394233</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SMC53992.2023.10394233" target="_blank" >10.1109/SMC53992.2023.10394233</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Investigating the Potential of AI-Driven Innovations for Enhancing Differential Evolution in Optimization Tasks

Popis výsledku v původním jazyce

In recent years, artificial intelligence (AI) and machine learning have demonstrated remarkable potential in various application domains, including optimization. This study investigates the process of leveraging AI, particularly large language models (LLMs), to enhance the performance of metaheuristics, with a focus on the well-established Differential Evolution (DE) algorithm. We employ GPT, a state-of-the-art LLM, to propose an improved mutation strategy based on a dynamic switching mechanism, which is then integrated into the DE algorithm. Throughout the investigation, we also observe and analyze any errors or limitations the LLM might exhibit. We conduct extensive experiments on a comprehensive set of 30 benchmark functions, comparing the performance of the proposed AI-inspired strategy with the standard DE algorithm. The results suggest that the AI-driven dynamic switching mutation strategy provides a competitive edge in terms of solution quality, showcasing the potential of using AI to guide the development of improved optimization algorithms. This work not only highlights the effectiveness of the proposed strategy but also contributes to the understanding of the process of using LLMs for enhancing metaheuristics and the challenges involved therein.

Název v anglickém jazyce

Investigating the Potential of AI-Driven Innovations for Enhancing Differential Evolution in Optimization Tasks

Popis výsledku anglicky

In recent years, artificial intelligence (AI) and machine learning have demonstrated remarkable potential in various application domains, including optimization. This study investigates the process of leveraging AI, particularly large language models (LLMs), to enhance the performance of metaheuristics, with a focus on the well-established Differential Evolution (DE) algorithm. We employ GPT, a state-of-the-art LLM, to propose an improved mutation strategy based on a dynamic switching mechanism, which is then integrated into the DE algorithm. Throughout the investigation, we also observe and analyze any errors or limitations the LLM might exhibit. We conduct extensive experiments on a comprehensive set of 30 benchmark functions, comparing the performance of the proposed AI-inspired strategy with the standard DE algorithm. The results suggest that the AI-driven dynamic switching mutation strategy provides a competitive edge in terms of solution quality, showcasing the potential of using AI to guide the development of improved optimization algorithms. This work not only highlights the effectiveness of the proposed strategy but also contributes to the understanding of the process of using LLMs for enhancing metaheuristics and the challenges involved therein.

Druh

D - Stať ve sborníku

CEP obor

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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

2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

ISBN

979-8-3503-3703-7

ISSN

1062-922X

e-ISSN

2577-1655

Počet stran výsledku

6

Strana od-do

1070-1075

Název nakladatele

IEEE

Místo vydání

New Jersey, Piscataway

Místo konání akce

Honolulu

Datum konání akce

1. 10. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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