Parameter optimization of SVM based on HQGA

SVM (Support Vector Machine) based on statistical learning theory and structural risk minimization principle has shown good performance and unique advantages in resolving the non-linear and high dimension problems with limited samples. However, the parameters of SVM have a significant impact on the identifying accuracy and generalization ability of SVM. It is based on the fact that this paper uses HQGA (Hybrid Quantum Genetic Algorithms) to optimize the parameters of SVM. HQGA combined with the excellent global optimization capability of QGA (Quantum Genetic Algorithms) and the excellent local optimization ability of GD (Gradient Descent) gives a better solution to the traditional problem on Parameter optimization of SVM. Final example demonstrates this algorithm very well.