Sleep staging is one of the important areas which is used to diagnose several diseases. People try to obtain models to carry out this operation without human interaction due to the time-consuming and complex nature of classification process. Most of the prior studies use concatenation of the extracted features from the electroencephalography (EEG) signals to obtain a single classifier. However, concatenating different feature views may not always yield better classification performance. This paper proposes a combination of kernels using the genetic algorithm based weight optimization process for sleep stage classification instead of concatenation. Unlike the previous works, our novelty is combining different feature views in a new structure with optimized kernel weights which are obtained from the genetic algorithm. In the proposed model SVM classifiers are trained by distinct feature views namely wavelet decomposition(DWT), autoregressive model based and frequency based energy features. Weighted linear combination of the single kernels is used to construct a new kernel and the performance of the model is compared with traditional kernel function. Experiments are carried out on 10 different patients. The average accuracy of the experiments is considered as final accuracy. The results show that the proposed architecture increases the performance up to approximately 86 % on average. The proposed structure fits better for multi-source data, unlike traditional single kernel methods.