Surrogate-assisted evolutionary algorithms (SAEAs) have been proposed to solve expensive optimization problems. Although SAEAs use surrogate models that approximate the evaluations of solutions using machine learning techniques, prior research has not adequately investigated the impact of surrogate model accuracy on search performance and model management strategy in SAEAs. This study analyzes how surrogate model accuracy affects search performance and model management strategies. For this purpose, we construct a pseudo-surrogate model with adjustable prediction accuracy to ensure fair comparisons across different model management strategies. We compared three model management strategies: (1) pre-selection (PS), (2) individual-based (IB), and (3) generation-based (GB) on standard benchmark problems with a baseline model that does not use surrogates. The experimental results reveal that a higher surrogate model accuracy improves the search performance. However, the impact varies according to the strategy used. Specifically, PS demonstrates a clear trend of improved performance as the estimation accuracy increases, whereas IB and GB exhibit robust performance when the accuracy surpasses a certain threshold. In model strategy comparisons, GB exhibits superior performance across a broad range of prediction accuracies, IB outperforms it at lower accuracies, and PS outperforms it at higher accuracies. The findings of this study clarify guidelines for selecting appropriate model management strategies based on the surrogate model accuracy.

翻译：代理辅助进化算法（SAEAs）已被提出用于求解昂贵优化问题。尽管SAEAs使用基于机器学习技术近似解评估的代理模型，但先前研究未能充分探究代理模型精度对SAEAs搜索性能及模型管理策略的影响。本研究分析了代理模型精度如何影响搜索性能与模型管理策略。为此，我们构建了具有可调节预测精度的伪代理模型，以确保在不同模型管理策略间进行公平比较。我们在标准基准问题上对比了三种模型管理策略：（1）预选择（PS）、（2）基于个体（IB）与（3）基于代次（GB），并以不使用代理的基线模型作为参照。实验结果表明，更高的代理模型精度能提升搜索性能，但其影响程度因策略而异。具体而言，PS在估计精度提高时展现出明确的性能改善趋势，而IB和GB在精度超过特定阈值后表现出稳健性能。在策略对比中，GB在较宽的预测精度范围内表现最优，IB在较低精度下优于GB，而PS在较高精度下超越GB。本研究结果明确了依据代理模型精度选择适宜模型管理策略的指导原则。