非高斯过程替代物预测的不确定度度量

IF 4.6 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Caie Hu;Sanyou Zeng;Changhe Li
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引用次数: 1

摘要

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的作用。它们可以增强算法的探索能力,提高代理的准确性。然而,目前尚无理论方法来测量非高斯过程的预测不确定度。为了解决这一问题,本文提出了一种测量不确定度的方法。该方法利用一个已知均值为零的平稳随机场来测量非高斯过程替代物预测的不确定性。实验分析表明,该方法能够测量非高斯过程的预测不确定度。在单代理和集成代理两种情况下的一组基准问题上验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Uncertainty Measure for Prediction of Non-Gaussian Process Surrogates
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.
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来源期刊
Evolutionary Computation
Evolutionary Computation 工程技术-计算机:理论方法
CiteScore
6.40
自引率
1.50%
发文量
20
审稿时长
3 months
期刊介绍: Evolutionary Computation is a leading journal in its field. It provides an international forum for facilitating and enhancing the exchange of information among researchers involved in both the theoretical and practical aspects of computational systems drawing their inspiration from nature, with particular emphasis on evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming, and genetic programming. It welcomes articles from related fields such as swarm intelligence (e.g. Ant Colony Optimization and Particle Swarm Optimization), and other nature-inspired computation paradigms (e.g. Artificial Immune Systems). As well as publishing articles describing theoretical and/or experimental work, the journal also welcomes application-focused papers describing breakthrough results in an application domain or methodological papers where the specificities of the real-world problem led to significant algorithmic improvements that could possibly be generalized to other areas.
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