AI 中文总结
本研究提出一种自适应集成代理辅助进化算法，通过优化径向基函数网络结构构建不同平滑度的鲁棒代理模型集成，在单目标基准与实际昂贵优化问题上表现优于现有先进算法。
AI 中文摘要
代理模型的集成有助于提升代理模型的预测质量与鲁棒性，进而提升代理辅助进化算法（SAEAs）的搜索性能。尽管为构建有效集成需要精心设计近似适应度景观的不同平滑度，但目前对代理模型衍生的平滑度程度的显式调优关注甚少。本研究提出一种自适应集成SAEA，可通过优化参数设置自动构建合理的集成模型。与现有仅考虑预测精度的自适应/集成SAEA不同，该算法通过求解近似误差与模型复杂度的双目标最小化问题优化径向基函数网络（RBFNs）的结构，从而得到兼具准确性、且近似适应度景观具有不同平滑度的鲁棒集成模型，减少了过拟合与欠拟合。此外，设计了一种填充准则，使具有不同平滑度的代理模型能为解预筛选提供帮助。实验结果表明，在昂贵优化场景下的单目标基准测试集与实际问题集上，该算法相比最先进的SAEAs具有统计优越性。该算法的源代码可在指定网址获取。
英文摘要
An ensemble of surrogate models helps improve the prediction quality and robustness of surrogate models, and in turn, the search performance of surrogate-assisted evolutionary algorithms (SAEAs). Although different degrees of smoothness of the approximated fitness landscapes need to be carefully designed for an effective ensemble, little attention has been paid to the explicit tuning of the degree of smoothness derived by surrogate models. This study proposes an adaptive ensemble SAEA, which automatically constructs plausible ensemble models by optimizing their parameter settings. Unlike existing adaptive/ensemble SAEAs, which consider prediction accuracy alone, the proposed algorithm optimizes the structure of radial basis function networks (RBFNs) by solving bi-objective minimization problems of approximation error and model complexity, resulting in robust ensemble models of accurate surrogate models with different degrees of smoothness of the approximated fitness landscapes. As a result, the over/under-fittings are reduced. Additionally, an infill criterion is designed so that surrogate models with different degrees of smoothness can contribute to the solution prescreening. The experimental results demonstrated the statistical superiority of our algorithm over state-of-the-art SAEAs on a single-objective benchmark and real-world problem sets under an expensive optimization scenario. The source code of the proposed algorithm is available at https://github.com/haranychan/EPOS
CommentsA preprint of the "Accepted article" version
Journal refIEEE Transactions on Cybernetics, Early Access, 2026
