Overfitting detection and adaptive covariant parsimony pressure for symbolic regression

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Kronberger:2011:GECCOcomp,
  author =       "Gabriel Kronberger and Michael Kommenda and 
                 Michael Affenzeller",
  title =        "Overfitting detection and adaptive covariant parsimony
                 pressure for symbolic regression",
  booktitle =    "3rd symbolic regression and modeling workshop for
                 GECCO 2011",
  year =         "2011",
  editor =       "Steven Gustafson and Ekaterina Vladislavleva",
  isbn13 =       "978-1-4503-0690-4",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "631--638",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001858.2002060",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Covariant parsimony pressure is a theoretically
                 motivated method primarily aimed to control bloat. In
                 this contribution we describe an adaptive method to
                 control covariant parsimony pressure that is aimed to
                 reduce overfitting in symbolic regression. The method
                 is based on the assumption that overfitting can be
                 reduced by controlling the evolution of program length.
                 Additionally, we propose an overfitting detection
                 criterion that is based on the correlation of the
                 fitness values on the training set and a validation set
                 of all models in the population.

                 The proposed method uses covariant parsimony pressure
                 to decrease the average program length when over
                 fitting occurs and allows an increase of the average
                 program length in the absence of overfitting. The
                 proposed approach is applied on two real world
                 datasets. The experimental results show that the
                 correlation of training and validation fitness can be
                 used as an indicator for overfitting and that the
                 proposed method of covariant parsimony pressure
                 adaption alleviates overfitting in symbolic regression
                 experiments with the two datasets.",
  notes =        "Also known as \cite{2002060} Distributed on CD-ROM at
                 GECCO-2011.

                 ACM Order Number 910112.",
}

Genetic Programming entries for Gabriel Kronberger Michael Kommenda Michael Affenzeller

Citations