A study on Genetic Programming with layered learning and incremental sampling

In this paper, we investigate the impact of a layered learning approach with incremental sampling on Genetic Programming (GP). The new system, called GPLL, is tested and compared with standard GP on twelve symbolic regression problems. While GPLL does not differ from standard GP on univariate target functions, it has better training efficiency on problems with bivariate targets. This indicates the potential usefulness of layered learning with incremental sampling in improving the efficiency of GP evolutionary learning.