Robust symbolic regression with affine arithmetic
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Pennachin:2010:gecco,
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author = "Cassio L. Pennachin and Moshe Looks and
Joao A. {de Vasconcelos}",
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title = "Robust symbolic regression with affine arithmetic",
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booktitle = "GECCO '10: Proceedings of the 12th annual conference
on Genetic and evolutionary computation",
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year = "2010",
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editor = "Juergen Branke and Martin Pelikan and Enrique Alba and
Dirk V. Arnold and Josh Bongard and
Anthony Brabazon and Juergen Branke and Martin V. Butz and
Jeff Clune and Myra Cohen and Kalyanmoy Deb and
Andries P Engelbrecht and Natalio Krasnogor and
Julian F. Miller and Michael O'Neill and Kumara Sastry and
Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and
Carsten Witt",
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isbn13 = "978-1-4503-0072-8",
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pages = "917--924",
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keywords = "genetic algorithms, genetic programming",
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month = "7-11 " # jul,
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organisation = "SIGEVO",
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address = "Portland, Oregon, USA",
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language = "en",
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oai = "oai:CiteSeerX.psu:10.1.1.308.3201",
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URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.3201",
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DOI = "doi:10.1145/1830483.1830648",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "We use affine arithmetic to improve both the
performance and the robustness of genetic programming
for symbolic regression. During evolution, we use
affine arithmetic to analyse expressions generated by
the genetic operators, estimating their output range
given the ranges of their inputs over the training
data. These estimated output ranges allow us to discard
trees that contain asymptotes as well as those whose
output is too far from the desired output range
determined by the training instances. We also perform
linear scaling of outputs before fitness evaluation.
Experiments are performed on 15 problems, comparing the
proposed system with a baseline genetic programming
system with protected operators, and with a similar
system based on interval arithmetic. Results show that
integrating affine arithmetic with an implementation of
standard genetic programming reduces the number of
fitness evaluations during training and improves
generalisation performance, minimises overfitting, and
completely avoids extreme errors of unseen test data.",
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notes = "Also known as \cite{1830648} GECCO-2010 A joint
meeting of the nineteenth international conference on
genetic algorithms (ICGA-2010) and the fifteenth annual
genetic programming conference (GP-2010)",
- }
Genetic Programming entries for
Cassio Pennachin
Moshe Looks
Joao Antonio de Vasconcelos
Citations