Parameter Optimization for Support Vector Machine Based on Nested Genetic Algorithms

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 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. 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. 