  Genetic programming (GP) is a commonly used approach to solve symbolic
regression (SR) problems. Compared with the machine learning or deep learning
methods that depend on the pre-defined model and the training dataset for
solving SR problems, GP is more focused on finding the solution in a search
space. Although GP has good performance on large-scale benchmarks, it randomly
transforms individuals to search results without taking advantage of the
characteristics of the dataset. So, the search process of GP is usually slow,
and the final results could be unstable.To guide GP by these characteristics,
we propose a new method for SR, called Taylor genetic programming (TaylorGP)
(Code and appendix at https://kgae-cup.github.io/TaylorGP/). TaylorGP leverages
a Taylor polynomial to approximate the symbolic equation that fits the dataset.
It also utilizes the Taylor polynomial to extract the features of the symbolic
equation: low order polynomial discrimination, variable separability, boundary,
monotonic, and parity. GP is enhanced by these Taylor polynomial techniques.
Experiments are conducted on three kinds of benchmarks: classical SR, machine
learning, and physics. The experimental results show that TaylorGP not only has
higher accuracy than the nine baseline methods, but also is faster in finding
stable results.
