Avoiding Overfitting in Symbolic Regression Using the First Order Derivative of GP Trees

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Samaneh Sadat {Mousavi Astarabadi} and 
                 Mohammad Mehdi Ebadzadeh",
  title =        "Avoiding Overfitting in Symbolic Regression Using the
                 First Order Derivative of GP Trees",
  booktitle =    "GECCO Companion '15: Proceedings of the Companion
                 Publication of the 2015 Annual Conference on Genetic
                 and Evolutionary Computation",
  year =         "2015",
  editor =       "Sara Silva and Anna I Esparcia-Alcazar and 
                 Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and 
                 Christine Zarges and Luis Correia and Terence Soule and 
                 Mario Giacobini and Ryan Urbanowicz and 
                 Youhei Akimoto and Tobias Glasmachers and 
                 Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and 
                 Marta Soto and Carlos Cotta and Francisco B. Pereira and 
                 Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and 
                 Heike Trautmann and Jean-Baptiste Mouret and 
                 Sebastian Risi and Ernesto Costa and Oliver Schuetze and 
                 Krzysztof Krawiec and Alberto Moraglio and 
                 Julian F. Miller and Pawel Widera and Stefano Cagnoni and 
                 JJ Merelo and Emma Hart and Leonardo Trujillo and 
                 Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and 
                 Carola Doerr",
  isbn13 =       "978-1-4503-3488-4",
  keywords =     "genetic algorithms, genetic programming: Poster",
  pages =        "1441--1442",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "http://doi.acm.org/10.1145/2739482.2764662",
  DOI =          "doi:10.1145/2739482.2764662",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Genetic programming (GP) is widely used for
                 constructing models with applications in control,
                 classification, regression, etc.; however, it has some
                 shortcomings, such as generalization. This paper
                 proposes to enhance the GP generalization by
                 controlling the first order derivative of GP trees in
                 the evolution process. To achieve this goal, a
                 multi-objective GP is implemented. Then, the first
                 order derivative of GP trees is considered as one of
                 its objectives. The proposed method is evaluated on
                 several benchmark problems to provide an experimental
                 validation. The experiments demonstrate the usefulness
                 of the proposed method with the capability of achieving
                 compact solutions with reasonable accuracy on training
                 data and better accuracy on test data.",
  notes =        "Also known as \cite{2764662} Distributed at

Genetic Programming entries for Samaneh Sadat Mousavi Astarabadi Mohammad Mehdi Ebadzadeh