tailieunhanh - Parameter optimization for support vector machine based on nested genetic algorithms
This paper presents an original method based on two nested realvalued 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. | Journal of Automation and Control Engineering Vol. 4, No. 1, February 2016 Parameter Optimization for Support Vector Machine Based on Nested Genetic Algorithms Pin Liao, Xin Zhang, and Kunlun Li College of Science and Technology, Nanchang University, Nanchang, China Email: pinliao@, zhangxin77@, likunlunnihao@ Yang Fu Tian Ge Interactive Holdings Limited, Beijing, China Email: fyten@ Mingyan Wang and Sensen Wang Information Engineering School, Nanchang University, Nanchang, China Email: mingyanw@, wsensen@ minimizing the model complexity. And kernel function parameters, such as the gamma γ for the radial basis function (RBF) kernel, define the non-linear mapping from the input space to some high-dimensional feature space. Just for convenience, this work uses RBF as the kernel function of SVM without loss of generality. Hence this paper proposes a novel method to optimize the parameters of SVM by nesting two real-valued genetic algorithms (NRGA). NRGA is tremendously efficient compared to the existing parameter optimization techniques which simultaneously search all the parameters. In NRGA, the inner-loop real-valued genetic algorithm (RGA) involves the optimization of penalty factor C with fixed kernel function parameters, and the outer-loop RGA is in charge of optimizing kernel function parameters. So that, in each step of the outer loop, the kernel values remain unchanged and can be reused for all iterations of the inner loop. Therefore, NRGA can reduce the computing time of the SVM parameter optimization by orders of magnitude (dependent on the iteration number of the inner loop) compared to the traditional optimization approaches. The remainder of this paper is organized as follows. Section II reviews pertinent literature on parameter optimization for SVM. Section III gives a brief introduction to SVM. Section VI describes the parameter optimization method based on two nested RGA. Section V presents the
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