This paper introduces an approach that integrates self-adaptive Evolution Strategies (ES) with Large Language Models (LLMs) to enhance the explainability of complex optimization processes. By employing a self-adaptive ES equipped with a restart mechanism, we effectively navigate the challenging landscapes of benchmark functions, capturing detailed logs of the optimization journey. The logs include fitness evolution, step-size adjustments and restart events due to stagnation. An LLM is then utilized to process these logs, generating concise, user-friendly summaries that highlight key aspects such as convergence behavior, optimal fitness achievements, and encounters with local optima. Our case study on the Rastrigin function demonstrates how our approach makes the complexities of ES optimization transparent. Our findings highlight the potential of using LLMs to bridge the gap between advanced optimization algorithms and their interpretability.

翻译：本文提出了一种将自适应进化策略与大型语言模型相结合的方法，旨在提升复杂优化过程的可解释性。通过采用配备重启机制的自适应进化策略，我们有效探索了基准函数的复杂优化空间，并完整记录了优化过程的详细日志。这些日志包含适应度演化轨迹、步长调整记录以及因停滞触发的重启事件。随后利用大型语言模型处理这些日志，生成简洁易懂的优化过程摘要，重点呈现收敛特性、最优适应度达成情况以及局部最优解遭遇等关键信息。以Rastrigin函数为对象的案例研究表明，该方法能够清晰揭示进化策略优化过程的复杂性。研究结果凸显了利用大型语言模型弥合先进优化算法与其可解释性之间鸿沟的潜力。