A memetic algorithm using emperor penguin and social engineering optimization for medical data classification

Abstract Gene selection and classification of microarray data play an important role in cancer diagnosis and treatment. One of the most popular and faster classification model is support vector machine (SVM). However, the major challenge in SVM lies in the selection of its two parameters, namely, regularization parameter C and kernel parameter γ . Attempts have been made to improve the performance of SVM by tuning these two parameters with the help of metaheuristics. Although existing metaheuristics can search the promising regions of the search space, they are unable to explore the global optimum efficiently. In this paper, a memetic algorithm-based SVM (M-SVM) is presented for simultaneous feature selection and optimization of SVM parameters. The memetic algorithm is a fusion of local search strategy using social engineering optimizer (SEO) and global optimization framework using emperor penguin optimizer (EPO). The idea of embedding SEO in EPO is to enhance the exploitation capability of EPO. The performance of our algorithm is evaluated on seven standard benchmark datasets. To prove the efficacy of the method, it is compared with particle swarm optimization based SVM (PSO-SVM), genetic algorithm based SVM (GA-SVM), and fifteen other state-of-the-art methods. The experimental results confirm that the proposed method significantly outperforms other existing techniques in terms of accuracy and number of selected genes. The proposed method is validated using a statistical analysis, namely, ANOVA.