Optimizing Hyperparameters of Support Vector Machines by Genetic Algorithms.

Abstract

Abstract — In this paper, a combination of genetic algorithms and support vector machines (SVMs) is proposed. SVMs are used for solving classification tasks, whereas genetic algorithms are optimization heuristics combining direct and stochastic search within a solution space. Here, the solution space is formed by combinations of different SVM’s kernel functions and kernel parameters. We investigate classification performance of evolutionary constructed SVMs in a complex real-world scenario of direct marketing.