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.

Surrogate-assisted evolutionary algorithms for addressing expensive optimization problems with both continuous and categorical variables (EOPCCVs) are still in the early stages of development. This study makes significant advancements by leveraging the mixed-variable nature of EOPCCVs in two crucial ways. First, it introduces a novel hybrid approach combining differential evolution and upper confidence bound sampling (DEUCB), designed to explore the mixed search space effectively. Second, a specialized value distance metric (VDM) is proposed, integrating continuous and categorical variables, to enhance the accuracy of the radial basis function (RBF) model approximation. Finally, we present a customized evolutionary expensive optimization algorithm (CEEO), which seamlessly incorporates DEUCB and RBF-VDM into the widely utilized global and local surrogate-assisted evolutionary optimization framework. Experimental results, compared against state-of-the-art counterparts on three distinct sets of benchmark problems and a convolutional neural network hyperparameter optimization task, consistently affirm the efficacy of the proposed CEEO in addressing EOPCCVs. The source code for the proposed CEEO algorithm is available athttps://github.com/CIAM-Group/EvolutionaryAlgorithm_Codes/tree/main/CEEO_Code.

Surrogate-assisted evolutionary algorithms (SAEAs) have proven to be effective in solving computationally expensive optimization problems (EOPs). However, the performance of SAEAs heavily relies on the surrogate model and infill criterion used. To improve the generalization of SAEAs and enable them to solve a wide range of EOPs, this paper proposes an SAEA called AutoSAEA, which features model and infill criterion auto-configuration. Specifically, AutoSAEA formulates model and infill criterion selection as a two-level multi-armed bandit problem (TL-MAB). The first and second levels cooperate in selecting the surrogate model and infill criterion, respectively. A two-level reward (TL-R) measures the value of the surrogate model and infill criterion, while a two-level upper confidence bound (TL-UCB) selects the model and infill criterion in an online manner. Numerous experiments validate the superiority of AutoSAEA over some state-of-the-art SAEAs on complex benchmark problems and a real-world oil reservoir production optimization problem.