Robust symbolic regression with affine arithmetic

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@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