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Parameter Optimization for Support Vector Machine Based on Nested Genetic Algorithms

Pin Liao 1, Xin Zhang 1, Kunlun Li 1, Yang Fu 2, Mingyan Wang 3, and Sensen Wang 3
1. College of Science and Technology, Nanchang University, Nanchang, China
2. Tian Ge Interactive Holdings Limited, Beijing, China
3. Information Engineering School, Nanchang University, Nanchang, China

Abstract—Support Vector Machine (SVM) is a popular and landmark classification method based on the idea of structural risk minimization, which has obtained extensive adoption across numerous domains such as pattern recognition, regression, ranking, etc. In order to achieve satisfying generalization, penalty and kernel function parameters of SVM must be carefully determined. This paper presents an original method based on two nested real-valued genetic algorithms (NRGA), which can optimize the parameters of SVM efficiently and speed up the parameter optimization by orders of magnitude compared to the traditional methods which optimize all the parameters simultaneously. As illustrated by the experimental results on gender classification of facial images, the proposed parameter optimization method, NRGA, can develop a SVM classifier quickly with superior classification accuracy due to its overwhelming efficiency and consequent searching power.

Index Terms—Support Vector Machine, parameter optimization, genetic algorithm, nested optimization method, gender classification of facial images

Cite: Pin Liao, Xin Zhang, Kunlun Li, Yang Fu, Mingyan Wang, and Sensen Wang, "Parameter Optimization for Support Vector Machine Based on Nested Genetic Algorithms," Jounal of Automation and Control Engineering, Vol. 4, No. 1, pp. 78-83, February, 2016. doi: 10.12720/joace.4.1.78-83
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