Kernel parameterization is a key design step in the application of support vector machines (SVM) for supervised learning problems. A grid-search with a cross-validation criteria is often conducted to choose the kernel parameters but it is computationally unfeasible for a large number of them. Here we describe a genetic algorithm (GA) as a method for tuning kernels of multiple parameters for classification tasks, with application to the weighted radial basis function (RBF) kernel. In this type of kernels the number of parameters equals the dimension of the input patterns which is usually high for biological datasets. We show preliminary experimental results where adapted weighted RBF kernels for SVM achieve classification performance over 98% in human serum proteomic profile data. Further improvements to this method may lead to discovery of relevant biomarkers in biomedical applications