A new genetic programming approach in symbolic regression

Genetic programming (GP) has been applied to symbolic regression problem for a long time. The symbolic regression is to discover a function that can fit a finite set of sample data. These sample data can be guided by a simple function, which is continuous and smooth, but in a complex system, the sample data can be produced by a discontinuous or non-smooth function. When conventional GP is applied to such complex system´s regression, it gets poor performance. This paper proposed a new GP representation and algorithm that can be applied to both continuous function´s regression and discontinuous function´s regression. The proposed approach is able to identify both the sub-functions and the discontinuity points simultaneously. The numerical experimental results show that the new GP is able to obtain higher success rate, higher convergence rate and better solutions than conventional GP in such complex system´s regression.