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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 …