The algorithm was based on GP-kernel and SVM. First, GP started with an initial population of randomly generated computer programs which are composed of functions and terminals, and their individuals were some nonlinear combination trees of kernel functions. Second, the SVM learning was performed for each individual tree. The following steps were performed iteratively until the termination criterion has been satisfied.Execute each program in the population and assign it a fitness value (classification accuracy) according to how well it solves the classification with SVM.Create a new population by applying the genetic operations.
