Journal Article

Compositional kernel learning using tree-based genetic programming for Gaussian process regression

Jin, Seung-Seop

2020

Overview

Although Gaussian process regression (GPR) is a powerful Bayesian nonparametric regression model for engineering problems, its predictive performance is highly dependent on a kernel for covariance function of GPR. However, choosing a proper kernel is still challenging even for experts. To choose proper kernel automatically, this study proposes a compositional kernel (CPK) learning using tree-based genetic programming (GEP). The optimal structure of the kernel is defined as a compositional representation based on sums and products of eight base-kernels. The CPK can be encoded as a tree-structure, so that tree-based GEP is employed to discover an optimal tree-structure of the CPK. To avoid overly complex solution in GEP, the proposed method introduced a dynamic maximum tree-depth technique. The novelty of the proposed method is to utilize more flexible and efficient learning capability to learn the relationship between input and output than existing methods. To evaluate the learning capability of the proposed method, seven test functions were firstly investigated with various noise levels, and its predictive accuracy was compared with existing methods. Reliability problems in both parallel and series systems were introduced to evaluate the performance of the proposed method for efficient reliability assessment. The results show that the proposed method generally outperforms or performs similarly to the best one among existing methods. In addition, it is also shown that proper kernel function can significantly improve the performance of GPR as the training data increases. Stated differently, the proposed method can learn the function of being fitted efficiently with less training samples than existing methods. In this context, the proposed method can make powerful and automatic predictive modeling based on GPR in engineering problems.

• Publisher: Springer Berlin Heidelberg,Springer Nature B.V
• Subject: Computational Mathematics and Numerical Analysis / Covariance / Engineering / Engineering Design / Gaussian process / Genetic algorithms / Kernel functions / Learning / Methods / Noise levels / Noise prediction / Performance enhancement / Performance evaluation / Performance prediction / Prediction models / Regression models / Reliability analysis / Reliability engineering / Research Paper / Systems analysis / Theoretical and Applied Mechanics / Training