Robust symbolic regression with affine arithmetic

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

@InProceedings{Pennachin:2010:gecco,

• author = "Cassio L. Pennachin and Moshe Looks and Joao A. {de Vasconcelos}",
• title = "Robust symbolic regression with affine arithmetic",
• booktitle = "GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation",
• year = "2010",
• 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",
• isbn13 = "978-1-4503-0072-8",
• pages = "917--924",
• keywords = "genetic algorithms, genetic programming",
• month = "7-11 " # jul,
• organisation = "SIGEVO",
• address = "Portland, Oregon, USA",
• language = "en",
• oai = "oai:CiteSeerX.psu:10.1.1.308.3201",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.3201",
• DOI = "doi:10.1145/1830483.1830648",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• 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.",
• 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