The support vector machine is a powerful classifier that has been successfully applied to a broad range of pattern recognition problems in various domains, e.g. corporate decision making, text and image recognition or medical diagnosis. Support vector machines belong to the group of semiparametric classifiers. The selection of appropriate parameters, formally known as model selection, is crucial to obtain accurate classification results for a given task. Striving to automate model selection for support vector machines we apply a meta-strategy utilizing genetic algorithms to learn combined kernels in a data-driven manner and to determine all free kernel parameters. The model selection criterion is incorporated into a fitness function guiding the evolutionary process of classifier construction. We consider two types of criteria consisting of empirical estimators or theoretical bounds for the generalization error. We evaluate their effectiveness in an empirical study on four well known benchmark data sets to find that both are applicable fitness measures for constructing accurate classifiers and conducting model selection. However, model selection focuses on finding one best classifier while genetic algorithms are based on the idea of re-combining and mutating a large number of good candidate classifiers to realize further improvements. It is shown that the empirical estimator is the superior fitness criterion in this sense, leading to a greater number of promising models on average.