Evolutionary tuning of multiple SVM parameters

We consider the problem of choosing multiple hyperparameters for support vector machines. We present a novel, general approach using an evolution strategy (ES) to determine the kernel from a parameterized kernel space and to control the regularization. We demonstrate on benchmark datasets that the ES improves the results achieved by grid search and can handle much more kernel parameters. In particular, we optimize generalized Gaussian kernels with arbitrary scaling and