Hybridization of Genetic and Quantum Algorithm for Gene Selection and Classification of Microarray Data

In this work, we hybridize the Genetic Quantum Algorithm with the Support Vector Machines classifier for gene selection and classification of high dimensional Microarray Data. We named our algorithm GQA(SV M). Its purpose is to identify a small subset of genes that could be used to separate two classes of samples with high accuracy. A comparison of the approach with different methods of literature, in particular GA(SV M) and PSOSV M [2], was realized on six different datasets issued of microarray experiments dealing with cancer (leukemia, breast, colon, ovarian, prostate, and lung) and available on Web. The experiments clearifzed the very good performances of the method. A first contribution shows that the algorithm GQA(SV M) is able to find genes of interest and improve the classification on a meaningful way. A second important contribution consists of the actual discovery of new and challenging results on datasets used.