SVR Kernel Parameters Selection Based on Steady-State Genetic Algorithm

The hyper parameters selection has a great affection on the accuracy of support vector regression algorithm. We chose the optimal hyper parameters including kernel parameters based on steady genetic algorithm for the support vector regression model. Selection of usually used RBF kernel parameters was thoroughly investigated. Two selection strategies for single and diagonal kernel parameters selection were applied on the standard sample data for Boston housing forecasting, and for electrical power demand forecasting. The testing results show that applying steady GA is effective in selecting multiple parameters