Large Language Models for Tuning Evolution Strategies

TL;DR

This paper introduces a feedback loop mechanism utilizing Large Language Models to automatically tune Evolution Strategies parameters, demonstrated through experiments on learning rate optimization with LLaMA3.

Contribution

It presents a novel method that leverages LLMs for iterative tuning of ES parameters, integrating code generation, execution, and analysis in a structured feedback loop.

Findings

Feasibility demonstrated on tuning ES learning rates with LLaMA3.

The method enables continuous refinement of ES parameters.

Potential for broader applications in optimization and algorithm tuning.

Abstract

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.