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.

翻译：大语言模型(LLMs)展现出世界知识与推理能力，使其成为各类应用中的强大工具。本文提出一种反馈循环机制，利用这些能力有效调优演化策略(ES)参数。该机制包含一个结构化流程：提供编程指令、执行相应代码并进行细致分析。该流程专门针对ES参数优化设计，通过迭代循环运行，确保ES参数的持续改进。首先，大语言模型处理指令以生成或修改代码；随后执行代码并详细记录结果；最后对结果进行分析，为后续优化提供洞见。基于LLaMA3模型对演化策略学习率调优的实验验证了该方法的可行性。本研究揭示了大语言模型如何被用于提升演化策略算法的性能，并指出类似反馈循环机制在多个领域的广阔应用前景。