  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.
