Genetic Algorithms with Improved Simulated Binary Crossover and Support Vector Regression for Grid Resources Prediction.

In order to manage the grid resources more effectively, the prediction information of grid resources is necessary in the grid system. This study developed a new model, ISGA-SVR, for parameters optimization in support vector regression (SVR), which is then applied to grid resources prediction. In order to build an effective SVR model, SVR΄s parameters must be selected carefully. Therefore, we develop genetic algorithms with improved simulated binary crossover (ISBX) that can automatically determine the optimal parameters of SVR with higher predictive accuracy. In ISBX, we proposed a new method to deal with the bounded search space. This method can improve the search ability of original simulated binary crossover (SBX) .The proposed model was tested with grid resources benchmark data set. Experimental results demonstrated that ISGA-SVR worked better than SVR optimized by genetic algorithm with SBX(SGA-SVR) and back-propagation neural network (BPNN). [ABSTRACT FROM AUTHOR]