Racing Control Variable Genetic Programming for Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Jiang_Xue_2024,

• author = "Nan Jiang and Yexiang Xue",
• title = "Racing Control Variable Genetic Programming for Symbolic Regression",
• booktitle = "Proceedings of the 38th AAAI Conference on Artificial Intelligence",
• year = "2024",
• pages = "12901--12909",
• keywords = "genetic algorithms, genetic programming",
• URL = "https://ojs.aaai.org/index.php/AAAI/article/view/29187",
• DOI = "doi:10.1609/aaai.v38i11.29187",
• size = "9 pages",
• abstract = "Symbolic regression, ... Control Variable Genetic Programming (CVGP) has been introduced which accelerates the regression process by discovering equations from designed control variable experiments. However, the set of experiments is fixed a-priori in CVGP and we observe that sub-optimal selection of experiment schedules delay the discovery process significantly. To overcome this limitation, we propose Racing Control Variable Genetic Programming (Racing-CVGP), which carries out multiple experiment schedules simultaneously. A selection scheme similar to that used in selecting good symbolic equations in genetic programming is implemented to ensure that promising experiment schedules eventually win over the average ones. The unfavorable schedules are terminated early to save time for the promising ones. We evaluate Racing-CVGP on several synthetic and real-world datasets corresponding to true physics laws. We demonstrate that Racing CVGP outperforms CVGP and a series of symbolic regressors which discover equations from fixed dataset",

}

Genetic Programming entries for Nan Jiang Yexiang Xue

Citations