An Uncertainty Measure for Prediction of Non-Gaussian Process Surrogates

Abstract

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 a theoretical method to measure the uncertainty of prediction of Non-Gaussian process surrogates. To address this issue, this paper 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.

ML-Based Detection of DDoS Attacks Using Evolutionary Algorithms Optimization

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