支持向量机
水准点(测量)
计算机科学
集合(抽象数据类型)
相关向量机
数学优化
进化算法
度量(数据仓库)
过程(计算)
高斯过程
机器学习
高斯分布
替代模型
人工智能
数据挖掘
算法
数学
物理
大地测量学
量子力学
程序设计语言
地理
操作系统
作者
Qing Zhang,Hanhua Zou,Zhihui Zeng,Sanyou Zeng
出处
期刊:Communications in computer and information science
日期:2022-01-01
卷期号:: 360-368
标识
DOI:10.1007/978-981-19-4109-2_33
摘要
In data-driven evolutionary optimization, surrogate model is usually used to replace expensive physical experiments or simulations to reduce the evaluation cost. When selecting promising individuals for exact evaluation, those with a large degree of uncertainty in approximation also need to be taken into account, which can strengthen the exploration ability of algorithms and improve the accuracy of surrogates. Support vector machine (SVM) is suitable for expensive small sample training set. However, it can only predict the mean value without the uncertainty. In order to solve this problem, the zero mean Gaussian random process is used to measure the uncertainty of support vector machine. The method’s efficiency is demonstrated on a set of benchmark problems. The results show that the uncertainty measurement method used in this paper improves the exploration ability of the evolutionary algorithm and obtains a better solution.
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