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Large Language Models for Tuning Evolution Strategies

Oliver Kramer

TL;DR

This work tackles the challenge of tuning Evolution Strategies (ES) parameters using feedback from Large Language Models (LLMs). It introduces a structured loop where an LLM translates tuning directives into executable code, runs experiments, logs results, and analyzes feedback to refine parameters. The demonstration tunes the learning-rate parameter $\tau$ of a $(1+1)$-ES with the Rechenberg rule on a $5$-dimensional Sphere function using the LLaMA3 framework, showing feasibility of LLM-guided optimization. The findings suggest a general, domain-agnostic framework for autonomous algorithm tuning with potential cross-domain impact.

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.

Large Language Models for Tuning Evolution Strategies

TL;DR

This work tackles the challenge of tuning Evolution Strategies (ES) parameters using feedback from Large Language Models (LLMs). It introduces a structured loop where an LLM translates tuning directives into executable code, runs experiments, logs results, and analyzes feedback to refine parameters. The demonstration tunes the learning-rate parameter of a -ES with the Rechenberg rule on a -dimensional Sphere function using the LLaMA3 framework, showing feasibility of LLM-guided optimization. The findings suggest a general, domain-agnostic framework for autonomous algorithm tuning with potential cross-domain impact.

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.
Paper Structure (11 sections, 2 equations, 2 figures)

This paper contains 11 sections, 2 equations, 2 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. LLM Tuning Evolution Strategies
  4. Evolution Strategies
  5. LLM Instructions
  6. Execution Process
  7. Log File
  8. Analysis
  9. Experimental Study
  10. Conclusions
  11. Benchmark Function

Figures (2)

  • Figure 1: Overview of LLM and ES interaction with exemplary prompts and code.
  • Figure 2: The plot illustrates that the LLM finds the optimal range for the parameter $\tau$ in the scenary of the (1+1)-ES on a 5-dimensional Sphere function (here maximizing $- \log f(\mathbf{x})$) with a proposed optimal value of $\tau = 0.95$.