Parameter Selection of Support Vector Regression Based on a Novel Chaotic Immune Algorithm

A novel chaotic immune algorithm (CImmune) is proposed to implement for parameter selection of Support Vector Regression (SVR). After adding chaotic local searching to the artificial immune procedure for parameter optimization of SVR, this method takes the advantages of both Chaos Optimization Algorithm (COA) and Artificial Immune Algorithm (AIA) to improve the effect of SVR efficiently. From experiments by cross validation on the simulated data and concrete compressive strength dataset from UCI, the results show that the proposed method has good capability of searching optimum and jumping out the local optimum easily in SVR model.