A Memetic Cellular Genetic Algorithm for Cancer Data Microarray Feature Selection

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 can be conveniently handled with optimization methods. This paper proposes a Memetic Cellular Genetic Algorithm (MCGA) to solve the Feature Selection problem of cancer microarray datasets. 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.