SVM is a powerful approach for pattern recognition especially for high dimensional, nonlinear problems. Recent developments on SVM have shown that it is necessary to consider multiple kernels [22]. This provides flexibility and reflects the fact that typical learning problems often involve multiple, heterogeneous data sources. Although the MKL-SVM algorithm is shown to improve the classification performance effectively, it relies more on the empirical kernel functions (e.g., polynomial function and radial basis function) and parameters (e.g., degree and Gaussian width), which can affect its effectiveness because different functions and parameters may result in different performances. A potential solution is to use GP to evolve the kernels and associated parameters automatically [39, 40].
