- author = "Hao Chen and Zi Yuan Guo and Hong Bai Duan and Duo Ban",
- journal = "IEEE Access",
- title = "A Genetic Programming-Driven Data Fitting Method",
- year = "2020",
- volume = "8",
- pages = "111448--111459",
- month = jun,
- keywords = "genetic algorithms, genetic programming, Data fitting, hybrid model, tree coding, interpretability.",
-
DOI = "
doi:10.1109/ACCESS.2020.3002563",
- ISSN = "2169-3536",
- size = "12 pages",
- abstract = "Data fitting is the process of constructing a curve, or a set of mathematical functions, that has the best fit to a series of data points. Different with constructing a fitting model from same type of function, such as the polynomial model, we notice that a hybrid fitting model with multiple types of function may have a better fitting result. Moreover, this also shows better interpretability. However, a perfect smooth hybrid fitting model depends on a reasonable combination of multiple functions and a set of effective parameters. That is a high-dimensional multi-objective optimization problem. This paper proposes a novel data fitting model construction approach. In this approach, the model is expressed by an improved tree coding expression and constructed through an evolution search process driven by the genetic programming. In order to verify the validity of generated hybrid fitting model, 6 prediction problems are chosen for experiment studies. The experimental results show that the proposed method is superior to 7 typical methods in terms of the prediction accuracy and interpretability.",
-
notes = "Also known as \cite{9117117}
School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, China",
