ABSTRACT:
Microarray data are expected to be of significant help in the development of efficient cancer diagnoses
and classification platforms. The main problem that needs to be addressed is the selection of a small
subset of genes from the thousands of genes in the data that contributes to a cancer disease. This
selection process is difficult due to the availability of a small number of samples compared to the huge
number of genes, many irrelevant genes, and noisy genes. Therefore, this paper proposes a cyclic
method based on genetic algorithms (GA) to select a near-optimal (small) subset of informative genes
that is relevant for cancer classification. The performance of the proposed method was evaluated by
three benchmark microarray data sets and obtained encouraging results as compared with other
experimented methods and previous related works.