Regularization regression based on real coded genetic algorithms

Real coded Genetic Algorithms (RGAs) have received a great deal of attention regarding their potential as optimization techniques for complex functions. Regularization was applied to solve ill-posed problems with an additional information about the solutions. In this paper, we introduce a new method named Regularization Regression based on RGAs to rebuild traditional regression methods, in which different regularization terms, regularization parameters and proper loss functions are designed flexibly according to prior knowledge of different problems.