Surrogate-assisted evolutionary algorithms (SAEAs) are well-suited for optimizing computationally expensive black-box problems in diverse real-world scenarios. The sample efficiency of SAEAs depends largely on the configuration of the surrogate model and sampling criteria. However, configuring these core components requires substantial manual effort and expert knowledge, limiting the broader applicability of SAEAs. To address these challenges, we propose CoE-SAEA, a novel paradigm that co-evolves large language models (LLMs) and configuration strategies to enhance SAEAs. Specifically, the paradigm consists of three populations with distinct roles: one evolves LLM prompts to generate robust configuration strategy instructions, another optimizes the configuration strategies, and the third solves the optimization problem using the selected algorithm configuration. Additionally, an exploration-exploitation module is incorporated to decide whether to explore new configuration strategies via LLMs or exploit existing ones. We empirically validate the efficacy of CoE-SAEA by comparing it to state-of-the-art algorithms across various benchmark problems and a real-world traffic signal optimization task. The source code of the proposed CoE-SAEA is publicly available at: https://github.com/ForrestXie9/CoE-SAEA.

Vehicle routing problems (VRP) are very important in many realworld applications and has been studied for several decades.Recently, neural combinatorial optimization (NCO) has attracted growing research effort.NCO is to train a neural network model to solve an optimization problem in question.However, existing NCO methods often build a different model for each routing problem, which significantly hinders their application in some areas where there are many different VRP variants to solve.In this work, we make a first attempt to tackle the crucial challenge of cross-problem generalization in NCO.We formulate VRPs as different combinations of a set of shared underlying attributes and solve them simultaneously via a single model through attribute composition.In this way, our proposed model can successfully solve VRPs with unseen attribute combinations in a zero-shot generalization manner.In our experiments, the neural model is trained on five VRP variants and its performance is tested on eleven VRP variants.The experimental results show that the model demonstrates superior performance on these eleven VRP variants, reducing the average gap to around 5% from over 20% and achieving a notable performance boost on both benchmark datasets and real-world logistics scenarios.