A new hybrid structure genetic programming 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, they can be produced by a discontinuous or non-smooth function. When conventional GP is applied to this complex system´s modelling, it gets poor performance. This paper proposes a new GP representation and algorithm that can be applied to both continuous function´s and discontinuous function´s regression. Our approach is able to identify both simultaneously the function´s structure and the discontinuity points. The numerical experimental results will show that the new GP is able to gain higher success rate, higher convergence rate and better solutions than conventional GP.