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European Conference on Genetic Programming

EuroGP 2017: Genetic Programming pp 114–130Cite as

RANSAC-GP: Dealing with Outliers in Symbolic Regression with Genetic Programming

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10196))

Abstract

Genetic programming (GP) has been shown to be a powerful tool for automatic modeling and program induction. It is often used to solve difficult symbolic regression tasks, with many examples in real-world domains. However, the robustness of GP-based approaches has not been substantially studied. In particular, the present work deals with the issue of outliers, data in the training set that represent severe errors in the measuring process. In general, a datum is considered an outlier when it sharply deviates from the true behavior of the system of interest. GP practitioners know that such data points usually bias the search and produce inaccurate models. Therefore, this work presents a hybrid methodology based on the RAndom SAmpling Consensus (RANSAC) algorithm and GP, which we call RANSAC-GP. RANSAC is an approach to deal with outliers in parameter estimation problems, widely used in computer vision and related fields. On the other hand, this work presents the first application of RANSAC to symbolic regression with GP, with impressive results. The proposed algorithm is able to deal with extreme amounts of contamination in the training set, evolving highly accurate models even when the amount of outliers reaches 90%.

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Notes

  1. 1.

    All results presented in this work are based on a standard GP implementation, using a tree representation, subtree crossover and subtree mutation.

  2. 2.

    An extensive discussion of these methods is beyond the scope of this work.

  3. 3.

    For all practitioners, it is intuitively evident that 10% of outliers can have drastic effects in the modeling process.

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Acknowledgments

First author was supported by CONACYT (México) scholarships No. 573397. This research was partially supported by CONACYT Basic Science Research Project No. 178323, CONACYT Fronteras de la Ciencia 2015-2 No. 944, as well as by FP7- Marie Curie-IRSES 2013 European Commission program with project ACoBSEC with contract No. 612689. Sara Silva acknowledges project PERSEIDS (PTDC/EMS-SIS/0642/2014) and BioISI RD unit, UID/MULTI/04046/2013, funded by FCT/MCTES/PIDDAC, Portugal.

Author information

Authors and Affiliations

  1. Posgrado en Ciencias de la Ingenieria, Instituto Tecnológico de Tijuana, Unidad Otay, Blvd. Industrial, Ave. ITR. Tijuana S/N, Mesa de Otay, C.P. 22500, Tijuana, Baja California, Mexico

    Uriel López, Leonardo Trujillo & Yuliana Martinez

  2. University of Bordeaux, 3ter Place de la Victoire, 33076, Bordeaux, France

    Pierrick Legrand

  3. IMB, UMR CNRS 5251, 351 Cours de la Libération, 33405, Talence, France

    Pierrick Legrand

  4. INRIA Bordeaux Sud-Ouest, 200 Rue Vieille Tour, 33405, Talence, France

    Pierrick Legrand

  5. Laboratorio Nacional de Geointeligencia (GeoINT), Centro de Investigación en Geografía y Geomática (CentroGeo), Aguascalientes, Mexico

    Enrique Naredo

  6. BioISI - Biosystems & Integrative Sciences Institute, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, 1749-016, Lisboa, Portugal

    Sara Silva

  7. CISUC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Sara Silva

Authors
  1. Uriel López
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  2. Leonardo Trujillo
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  3. Yuliana Martinez
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  4. Pierrick Legrand
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  5. Enrique Naredo
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  6. Sara Silva
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Corresponding author

Correspondence to Leonardo Trujillo .

Editor information

Editors and Affiliations

  1. University College Dublin , Dublin, Ireland

    James McDermott

  2. Universidade Nova de Lisboa , Lisbon, Portugal

    Mauro Castelli

  3. Brno University of Technology , Brno, Czech Republic

    Lukas Sekanina

  4. Vrije Universiteit Amsterdam , Amsterdam, The Netherlands

    Evert Haasdijk

  5. University of Cádiz , Cádiz, Spain

    Pablo García-Sánchez

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López, U., Trujillo, L., Martinez, Y., Legrand, P., Naredo, E., Silva, S. (2017). RANSAC-GP: Dealing with Outliers in Symbolic Regression with Genetic Programming. In: McDermott, J., Castelli, M., Sekanina, L., Haasdijk, E., García-Sánchez, P. (eds) Genetic Programming. EuroGP 2017. Lecture Notes in Computer Science(), vol 10196. Springer, Cham. https://doi.org/10.1007/978-3-319-55696-3_8

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  • DOI: https://doi.org/10.1007/978-3-319-55696-3_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55695-6

  • Online ISBN: 978-3-319-55696-3

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