Model management is an essential component in data-driven surrogate-assisted evolutionary optimization. In model management, the solutions with a large degree of uncertainty in approximation play an important role. They can strengthen the exploration ability of algorithms and improve the accuracy of surrogates. However, there is no theoretical method to measure the uncertainty of prediction of Non-Gaussian process surrogates. To address this issue, this article proposes a method to measure the uncertainty. In this method, a stationary random field with a known zero mean is used to measure the uncertainty of prediction of Non-Gaussian process surrogates. Based on experimental analyses, this method is able to measure the uncertainty of prediction of Non-Gaussian process surrogates. The method's effectiveness is demonstrated on a set of benchmark problems in single surrogate and ensemble surrogates cases.

模型管理是数据驱动的代理辅助进化优化的重要组成部分。在模型管理中，具有较大近似不确定性的解起着重要作用。它们可以加强算法的探索能力，提高代理的准确性。然而，没有理论方法来衡量非高斯过程代理预测的不确定性。针对这一问题，本文提出了一种测量不确定性的方法。在该方法中，使用已知零均值的平稳随机场来衡量非高斯过程替代项预测的不确定性。基于实验分析，该方法能够测量非高斯过程代理预测的不确定性。方法'