Large Language Models for the Automated Analysis of Optimization
Algorithms
- URL: http://arxiv.org/abs/2402.08472v1
- Date: Tue, 13 Feb 2024 14:05:02 GMT
- Title: Large Language Models for the Automated Analysis of Optimization
Algorithms
- Authors: Camilo Chac\'on Sartori and Christian Blum and Gabriela Ochoa
- Abstract summary: We aim to demonstrate the potential of Large Language Models (LLMs) within the realm of optimization algorithms by integrating them into STNWeb.
This is a web-based tool for the generation of Search Trajectory Networks (STNs), which are visualizations of optimization algorithm behavior.
- Score: 0.9668407688201361
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The ability of Large Language Models (LLMs) to generate high-quality text and
code has fuelled their rise in popularity. In this paper, we aim to demonstrate
the potential of LLMs within the realm of optimization algorithms by
integrating them into STNWeb. This is a web-based tool for the generation of
Search Trajectory Networks (STNs), which are visualizations of optimization
algorithm behavior. Although visualizations produced by STNWeb can be very
informative for algorithm designers, they often require a certain level of
prior knowledge to be interpreted. In an attempt to bridge this knowledge gap,
we have incorporated LLMs, specifically GPT-4, into STNWeb to produce extensive
written reports, complemented by automatically generated plots, thereby
enhancing the user experience and reducing the barriers to the adoption of this
tool by the research community. Moreover, our approach can be expanded to other
tools from the optimization community, showcasing the versatility and potential
of LLMs in this field.
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