Strength pareto evolutionary algorithm based gene subset selection

Microarray gene expression data is voluminous and very few genes in the dataset are informative for disease analysis. Selecting those genes from the whole dataset is a very challenging task. There are many optimization techniques used by the researchers for gene subset selection but none of them provides global optimal solution for all gene datasets. In the paper, we have proposed a strength pareto evolutionary algorithm based gene subset selection technique to select the informative gene subset for analyzing and identifying the disease efficiently. It is a multi-objective optimization algorithm that provides a non-dominated pareto front exploring the search space to obtain an optimal gene subset. The external cluster validation index and number of genes in a sample are considered as two objective functions of the algorithm and based on this two objective functions the chromosomes in the population are evaluated and after the convergence of the algorithm, chromosomes in the non-dominated pareto front gives the important gene subset. The experimental result on selected gene subset proves the usefulness of the method.