Improved Binary Imperialist Competition Algorithm for Feature Selection from Gene Expression Data

Gene expression profiles which represent the state of a cell at a molecular level could likely be important in the progress of classification platforms and proficient cancer diagnoses. In this paper, we attempt to apply imperialist competition algorithm (ICA) with parallel computation and faster convergence speed to select the least number of informative genes. However, ICA same as the other evolutionary algorithms is easy to fall into local optimum. In order to avoid the defect, we propose an improved binary ICA (IBICA) with the idea that the local best city (imperialist) in an empire is reset to the zero position when its fitness value does not change after five consecutive iterations. Then IBICA is empirically applied to a suite of well-known benchmark gene expression datasets. Experimental results show that the classification accuracy and the number of selected genes are superior to other previous related works.