Computational Analysis of gene expression data is extremely difficult, due to the existence of a huge number of genes and less number of samples (limited number of patients). Thus,it is of significant importance to provide a subset of the most informative genesto a learning algorithm, for constructing robust prediction models. In this study, we propose a hybrid Intelligent Water Drop (IWD) - Support Vector Machines (SVM) algorithm, with weighted gene ranking as a heuristic, for simultaneous gene subset selection and cancer prediction. Our results, evaluated on three cancer datasets, demonstrate that the genes selected by the IWD technique yield classification accuracies comparable to previously reported algorithms. © Springer-Verlag 2013.