Surrogate-assisted evolutionary algorithms (SAEAs) have emerged as a powerful class of optimization methods that utilize surrogate models to address expensive optimization problems (EOPs), where fitness evaluations (FEs) are expensive or limited. By leveraging previously evaluated solutions to learn predictive models, SAEAs enable efficient search under constrained evaluation budgets. However, the performance of SAEAs heavily depends on the quality and utilization of surrogate models, and balancing the accuracy and generalization ability makes effective model construction and management a key challenge. Therefore, this paper introduces a novel probability selection-based surrogate-assisted evolutionary algorithm (PS-SAEA) to enhance optimization performance under FE-constrained conditions. The PS-SAEA has two novel designs. First, a probabilistic model selection (PMS) strategy is proposed to stochastically select surrogate models, striking a balance between prediction accuracy and generalization by avoiding overfitting commonly caused by greedy selection. Second, a weighted model ensemble (WME) mechanism is developed to integrate selected models, assigning weights based on individual prediction errors to improve the accuracy and reliability of fitness estimation. Extensive experiments on benchmark problems with varying dimensionalities demonstrate that PS-SAEA consistently outperforms several state-of-the-art SAEAs, validating its effectiveness and robustness in dealing with various complex EOPs.
Wang et al. (Fri,) studied this question.