Racing Control Variable Genetic Programming for Symbolic Regression
Created by W.Langdon from
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- @InProceedings{Jiang_Xue_2024,
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author = "Nan Jiang and Yexiang Xue",
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title = "Racing Control Variable Genetic Programming for
Symbolic Regression",
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booktitle = "Proceedings of the 38th AAAI Conference on Artificial
Intelligence",
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year = "2024",
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pages = "12901--12909",
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keywords = "genetic algorithms, genetic programming",
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URL = "https://ojs.aaai.org/index.php/AAAI/article/view/29187",
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DOI = "doi:10.1609/aaai.v38i11.29187",
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size = "9 pages",
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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