  Large Language Models (LLMs) exhibit world knowledge and inference
capabilities, making them powerful tools for various applications. This paper
proposes a feedback loop mechanism that leverages these capabilities to tune
Evolution Strategies (ES) parameters effectively. The mechanism involves a
structured process of providing programming instructions, executing the
corresponding code, and conducting thorough analysis. This process is
specifically designed for the optimization of ES parameters. The method
operates through an iterative cycle, ensuring continuous refinement of the ES
parameters. First, LLMs process the instructions to generate or modify the
code. The code is then executed, and the results are meticulously logged.
Subsequent analysis of these results provides insights that drive further
improvements. An experiment on tuning the learning rates of ES using the LLaMA3
model demonstrate the feasibility of this approach. This research illustrates
how LLMs can be harnessed to improve ES algorithms' performance and suggests
broader applications for similar feedback loop mechanisms in various domains.
