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.