Microarray data are expected to be useful for cancer classification. The main problem that needs to be addressed is the selection of a smaller subset of genes from the thousands of genes in the data that contributes to a disease. This selection process is difficult due to many irrelevant genes, noisy genes, and the availability of a small number of samples compared to a huge number of genes (higher-dimensional data). Hence, this paper aims to select a near-optimal (smaller) subset of informative genes that is most relevant for the cancer classification. To achieve the aim, an iterative approach based on genetic algorithms has been proposed. Experimental results show that the performance of the proposed approach is superior to other related previous works as well as four methods experimented in this work. In addition a list of informative genes in the best gene subsets is also presented for biological usage.