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

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Lopez:2017:EuroGP,
  author =       "Uriel Lopez and Leonardo Trujillo and 
                 Yuliana Martinez and Pierrick Legrand and Enrique Naredo and 
                 Sara Silva",
  title =        "RANSAC-GP: Dealing with Outliers in Symbolic
                 Regression with Genetic Programming",
  booktitle =    "EuroGP 2017: Proceedings of the 20th European
                 Conference on Genetic Programming",
  year =         "2017",
  month =        "19-21 " # apr,
  editor =       "Mauro Castelli and James McDermott and 
                 Lukas Sekanina",
  series =       "LNCS",
  volume =       "10196",
  publisher =    "Springer Verlag",
  address =      "Amsterdam",
  pages =        "114--130",
  organisation = "species",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1007/978-3-319-55696-3_8",
  abstract =     "Genetic programming (GP) has been shown to be a
                 powerful tool for automatic modelling 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
                 behaviour 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 90percent.",
  notes =        "Part of \cite{Castelli:2017:GP} EuroGP'2017 held
                 inconjunction with EvoCOP2017, EvoMusArt2017 and
                 EvoApplications2017",
}

Genetic Programming entries for Uriel Lopez Leonardo Trujillo Yuliana Martinez Pierrick Legrand Enrique Naredo Sara Silva

Citations