Baihe He, Qiang Lu, Qingyun Yang, Jake Luo, Zhiguang Wang
TL;DRTaylor 遗传编程是一种利用 Taylor 多项式来提取符号方程特征的 SR 问题解决方法,实验证明 Taylor 遗传编程比其他九种基准方法更准确且更快。
Abstract
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
Symbolic regression is improved by Racing Control Variable Genetic Programming (Racing-CVGP), which carries out multiple experiment schedules simultaneously and outperforms other regressors.