Quantifying Uncertainties of Residuals in Symbolic Regression via Kriging
Created by W.Langdon from
gp-bibliography.bib Revision:1.7403
- @Article{YANG:2022:procs,
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author = "Kaifeng Yang and Michael Affenzeller",
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title = "Quantifying Uncertainties of Residuals in Symbolic
Regression via Kriging",
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journal = "Procedia Computer Science",
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volume = "200",
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pages = "954--961",
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year = "2022",
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note = "3rd International Conference on Industry 4.0 and Smart
Manufacturing",
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keywords = "genetic algorithms, genetic programming, White-box
modelling, Gaussian Processes, Kriging, Residuals,
Uncertainties",
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ISSN = "1877-0509",
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URL = "
https://www.sciencedirect.com/science/article/pii/S1877050922003027",
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DOI = "
doi:10.1016/j.procs.2022.01.293",
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abstract = "Genetic programming (GP) based symbolic regression is
a powerful technique for white-box modelling. However,
the prediction uncertainties of the symbolic regression
are still unknown. This paper proposes to use Kriging
to model the residual of a symbolic expression. The
residual model follows a normal distribution with
parameters of a mean value and a standard deviation,
where the mean value can be used to regulate the
prediction and the standard deviation represents the
uncertainties of residuals in a specific symbolic
expression. The proposed algorithms are compared with a
canonical GP-based symbolic regression and Kriging
regression on three benchmarks in symbolic regression
field. The results show that the proposed algorithm
significantly outperforms the other two algorithms on
the three benchmarks w.r.t. mean squared error in the
test dataset with a small generation budget",
- }
Genetic Programming entries for
Kaifeng Yang
Michael Affenzeller
Citations