A hybrid GA/SVM approach for gene selection and classification of microarray data

We propose a Genetic Algorithm (CA) approach combined with Support Vector Machines (SVM) for the classification of high dimensional Microarray data. This approach is associated to a fuzzy logic based pre-filtering technique. The CA is used to evolve gene subsets whose fitness is evaluated by a SVM classifier. Using archive records of "good" gene subsets, a frequency based technique is introduced to identify the most informative genes. Our approach is assessed on two well-known cancer datasets and shows competitive results with six existing methods.