A novel hybrid genetic algorithm and Simulated Annealing for feature selection and kernel optimization in support vector regression

In this paper, an effective hybrid optimization strategy by incorporating the metropolis acceptance criterion of Simulated Annealing (SA) into crossover operator of Genetic Algorithm (GA), is used to simultaneously optimize the input feature subset selection, the type of kernel function and the kernel parameter setting of SVR, namely GASA-SVR. The developed GASA-SVR model is being applied for monthly rainfall forecasting and flood management in Liuzhou, Guangxi. The GASA-SVR can increase the diversity of the individuals, accelerate the evolution process and avoid sinking into the local optimal solution early that compared with pure GA-SVR. Results show that the new GASA-SVR model can correctly select the discriminating input features, also successfully identify the optimal type of kernel function and all the optimal values of the parameters of SVR with the lowest prediction error values in rainfall forecasting.