Large Language Models (LLMs) have achieved significant progress across various fields and have exhibited strong potential in evolutionary computation, such as generating new solutions and automating algorithm design. Surrogate-assisted selection is a core step in evolutionary algorithms to solve expensive optimization problems by reducing the number of real evaluations. Traditionally, this has relied on conventional machine learning methods, leveraging historical evaluated evaluations to predict the performance of new solutions. In this work, we propose a novel surrogate model based purely on LLM inference capabilities, eliminating the need for training. Specifically, we formulate model-assisted selection as a classification and regression problem, utilizing LLMs to directly evaluate the quality of new solutions based on historical data. This involves predicting whether a solution is good or bad, or approximating its value. This approach is then integrated into evolutionary algorithms, termed LLM-assisted EA (LAEA). Detailed experiments compared the visualization results of 2D data from 9 mainstream LLMs, as well as their performance on optimization problems. The experimental results demonstrate that LLMs have significant potential as surrogate models in evolutionary computation, achieving performance comparable to traditional surrogate models only using inference. This work offers new insights into the application of LLMs in evolutionary computation. Code is available at: https://github.com/hhyqhh/LAEA.git

翻译：大型语言模型（LLMs）已在多个领域取得显著进展，并在进化计算中展现出强大潜力，例如生成新解和自动化算法设计。代理辅助选择是进化算法中解决昂贵优化问题的核心步骤，旨在通过减少真实评估次数来提高效率。传统方法通常依赖于常规机器学习技术，利用历史评估数据来预测新解的性能。在本工作中，我们提出了一种完全基于LLM推理能力的新型代理模型，无需训练过程。具体而言，我们将模型辅助选择构建为分类与回归问题，利用LLMs直接根据历史数据评估新解的质量——包括判断解的优势或近似其数值。该方法被整合到进化算法中，称为LLM辅助进化算法（LAEA）。通过详细实验，我们比较了9种主流LLM在二维数据上的可视化结果及其在优化问题中的表现。实验结果表明，LLMs仅通过推理即可达到与传统代理模型相当的性能，展现出作为进化计算代理模型的巨大潜力。本研究为LLMs在进化计算中的应用提供了新的视角。代码已开源：https://github.com/hhyqhh/LAEA.git