Gene expression microarray data is expected to significantly aid in the development of efficient cancer diagnosis and classification platforms. One problem arising from this data is how to select a small subset of genes from thousands of genes and much fewer samples that are inherently noisy. This research deals with finding a small subset of informative genes from the gene expression data which maximize the classification accuracy and minimize the running time. This paper proposed a mo...