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Abstract Compared with support vector machines(SVM)，Gaussian process(GP) holds many advantages such as easy coding，self-adaptive acquisition of hyper-parameters and prediction with probability interpretation. Herein，the Gaussian process regression(GPR) is adopted to analyze the slope displacement time series. A combined kernel function of GPR(CKGPR) obtained by additive single standard covariance functions is putted forward to overcome poor generalization ability of single kernel function. At present，the hyper-parameters of GPR are achieved by maximizing likelihood function of training samples based on conjugate gradient algorithm. However，the conjugate gradient algorithm has the shortcomings of too strong dependence on initial value in optimization effect，showing the difficultly in determination of iteration steps and easily falling into local optimum. On the basis of above-mentioned results，genetic algorithm(GA) coded in decimal system is used to optimize the hyper- parameters of GPR with the combined kernel function，and then the GA-CKGPR algorithm can be formed；and the corresponding code is programmed in Matlab. From the analytical results of Wolongsi slope displacement time series，it can be concluded that the GA-CKGPR algorithm can obviously improve the prediction precision than those of GA-SVR and standard GA-GPR algorithms，so it can be utilized in slope displacement analysis and meanwhile can be served as a reference for similar projects.
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Received: 10 November 2008
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