A hybrid discrete imperialist competition algorithm for gene selection for microarray data

Objective and Background: This paper presents a hybrid imperialist competition algorithm (ICA) for feature selection from microarray gene expression data. As we all known, ICA performs global search well by parallel searching. However, the population evolution only depends on assimilation mechanism and the algorithm has slow convergence speed. Therefore, a learning mechanism among imperialists is used to speed up the evolution of the population and accelerate the convergence velocity of the algorithm. Method: ICA is a kind of random search method. In order to select as many informative genes as possible, this paper presents a hybrid ICA combined with information entropy, which called as ICAIE. In the proposed algorithm, we utilize information entropy to locate genes and the roulette wheel selection mechanism to avoid the informative gene excessively selected. The proposed algorithm was tested on 10 standard gene expression datasets. Results and Conclusion: From the experiment, outcomes manifest that the performance of the presented algorithm is superior to different PSO-related (particle swarm optimization) and ICA-based algorithms in view of classification accuracy and the amount of targeted informative genes. Therefore, ICAIE is a very excellent method for feature selection.