# Automatic Adaptation Rule Optimization via Large Language Models

arXiv: https://arxiv.org/abs/2407.02203 · HTML: https://mlsanity.com/paper/2407.02203/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 7 references that are in the map.
- First posted on arXiv 2 Jul 2024.

## Builds on (most cited first, 4 of 4)

- [Large Language Models as Optimizers](https://mlsanity.com/paper/2309.03409/) (2023)
- [Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering](https://mlsanity.com/paper/2305.03403/) (2023)
- [Exploring the Potential of Large Language Models in Self-adaptive Systems](https://mlsanity.com/paper/2401.07534/) (2024)
- [Exploring the Improvement of Evolutionary Computation via Large Language Models](https://mlsanity.com/paper/2405.02876/) (2024)

## 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.