Taylor genetic programming for symbolic regression
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�…
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�…
[CITATION][C] Taylor Genetic Programming for Symbolic Regression. arXiv (2022)
B He, Q Lu, Q Yang, J Luo, Z Wang�- arXiv preprint arXiv:2205.09751
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