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�…