# Large Language Models as Surrogate Models in Evolutionary Algorithms: A Preliminary Study

arXiv: https://arxiv.org/abs/2406.10675 · HTML: https://mlsanity.com/paper/2406.10675/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2024.

## Built on by (most cited first, 20 of 20)

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- [A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving](https://mlsanity.com/paper/2509.08269/) (2025)
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- [Learn to Relax with Large Language Models: Solving Constraint Optimization Problems via Bidirectional Coevolution](https://mlsanity.com/paper/2509.12643/) (2025)
- [Elicitation Matters: How Prompts and Query Protocols Shape LLM Surrogates under Sparse Observations](https://mlsanity.com/paper/2605.04764/) (2026)
- [An Evolutionary Large Language Model for Hallucination Mitigation](https://mlsanity.com/paper/2412.02790/) (2024)
- [An Overview and Discussion on Using Large Language Models for Implementation Generation of Solutions to Open-Ended Problems](https://mlsanity.com/paper/2501.00562/) (2024)
- [Physics-Informed Evolution: An Evolutionary Framework for Solving Quantum Control Problems Involving the Schr"odinger Equation](https://mlsanity.com/paper/2502.05228/) (2025)
- [Relation Reasoning with LLMs in Expensive Optimization](https://mlsanity.com/paper/2605.02933/) (2026)
- [LLM-driven design of physics-constrained constitutive models: two agents are better than one](https://mlsanity.com/paper/2605.23754/) (2026)

## Abstract (arXiv)

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