A bootstrapping approach to reduce over-fitting in genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Jeannie Fitzgerald and R. Muhammad Atif Azad and 
                 Conor Ryan",
  title =        "A bootstrapping approach to reduce over-fitting in
                 genetic programming",
  booktitle =    "GECCO '13 Companion: Proceeding of the fifteenth
                 annual conference companion on Genetic and evolutionary
                 computation conference companion",
  year =         "2013",
  editor =       "Christian Blum and Enrique Alba and 
                 Thomas Bartz-Beielstein and Daniele Loiacono and 
                 Francisco Luna and Joern Mehnen and Gabriela Ochoa and 
                 Mike Preuss and Emilia Tantar and Leonardo Vanneschi and 
                 Kent McClymont and Ed Keedwell and Emma Hart and 
                 Kevin Sim and Steven Gustafson and 
                 Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and 
                 Nikolaus Hansen and Olaf Mersmann and Petr Posik and 
                 Heike Trautmann and Muhammad Iqbal and Kamran Shafi and 
                 Ryan Urbanowicz and Stefan Wagner and 
                 Michael Affenzeller and David Walker and Richard Everson and 
                 Jonathan Fieldsend and Forrest Stonedahl and 
                 William Rand and Stephen L. Smith and Stefano Cagnoni and 
                 Robert M. Patton and Gisele L. Pappa and 
                 John Woodward and Jerry Swan and Krzysztof Krawiec and 
                 Alexandru-Adrian Tantar and Peter A. N. Bosman and 
                 Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and 
                 David L. Gonzalez-Alvarez and 
                 Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and 
                 Kenneth Holladay and Tea Tusar and Boris Naujoks",
  isbn13 =       "978-1-4503-1964-5",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "1113--1120",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2464576.2482690",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Historically, the quality of a solution in Genetic
                 Programming (GP) was often assessed based on its
                 performance on a given training sample. However, in
                 Machine Learning, we are more interested in achieving
                 reliable estimates of the quality of the evolving
                 individuals on unseen data. In this paper, we propose
                 to simulate the effect of unseen data during training
                 without actually using any additional data. We do this
                 by employing a technique called bootstrapping that
                 repeatedly re-samples with replacement from the
                 training data and helps estimate sensitivity of the
                 individual in question to small variations across these
                 re-sampled data sets. We minimise this sensitivity, as
                 measured by the Bootstrap Standard Error, together with
                 the training error, in an effort to evolve models that
                 generalise better to the unseen data.

                 We evaluate the proposed technique on four binary
                 classification problems and compare with a standard GP
                 approach. The results show that for the problems
                 undertaken, the proposed method not only generalises
                 significantly better than standard GP while the
                 training performance improves, but also demonstrates a
                 strong side effect of containing the tree sizes.",
  notes =        "Also known as \cite{2482690} Distributed at

Genetic Programming entries for Jeannie Fitzgerald R Muhammad Atif Azad Conor Ryan