Automatic Adaptation Rule Optimization via Large Language Models
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Abstract (arXiv)
Rule-based adaptation is a foundational approach to self-adaptation, characterized by its human readability and rapid response. However, building high-performance and robust adaptation rules is often a challenge because it essentially involves searching the optimal design in a complex (variables) space. In response, this paper attempt to employ large language models (LLMs) as a optimizer to construct and optimize adaptation rules, leveraging the common sense and reasoning capabilities inherent in LLMs. Preliminary experiments conducted in SWIM have validated the effectiveness and limitation of our method.
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References · 4
- 2023Large Language Models as Optimizerscited by 864
- 2023Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineeringcited by 127
- 2024Exploring the Potential of Large Language Models in Self-adaptive Systemscited by 7
- 2024Exploring the Improvement of Evolutionary Computation via Large Language Modelscited by 4
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