A Memetic Cellular Genetic Algorithm for Cancer Data Microarray Feature Selection

Abstract

Gene selection aims at identifying a -small- subset of informative genes from the initial data to obtain high predictive accuracy for classification in human cancers. Gene selection can be considered as a combinatorial search problem and thus be conveniently handled with optimization methods. This paper focuses on feature subset selection for dimensionality reduction in cancer classification and prediction. In this work, a Memetic Cellular Genetic Algorithm (MCGA) to solve the Feature Selection problem of cancer microarray dataset is presented. Benchmark gene expression datasets, i.e., colon, lymphoma, and leukaemia available in the literature were used for experimentation. MCGA is compared with other well-known metaheuristic' strategies. The results demonstrate that our proposal can provide efficient solutions to find a minimal subset of the genes.