  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
