Improving Generalisation of Genetic Programming for Symbolic Regression with Structural Risk Minimisation

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

@InProceedings{Chen:2016:GECCO,
  author =       "Qi Chen and Mengjie Zhang and Bing Cue",
  title =        "Improving Generalisation of Genetic Programming for
                 Symbolic Regression with Structural Risk Minimisation",
  booktitle =    "GECCO '16: Proceedings of the 2016 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2016",
  editor =       "Tobias Friedrich",
  pages =        "709--716",
  keywords =     "genetic algorithms, genetic programming",
  month =        "20-24 " # jul,
  organisation = "SIGEVO",
  address =      "Denver, USA",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  isbn13 =       "978-1-4503-4206-3",
  DOI =          "doi:10.1145/2908812.2908842",
  abstract =     "Generalisation is one of the most important
                 performance measures for any learning algorithm, no
                 exception to Genetic Programming (GP). A number of
                 works have been devoted to improve the generalisation
                 ability of GP for symbolic regression. Methods based on
                 a reliable estimation of generalisation error of models
                 during evolutionary process are a sensible choice to
                 enhance the generalisation of GP. Structural risk
                 minimisation (SRM), which is based on the VC dimension
                 in the learning theory, provides a powerful framework
                 for estimating the difference between the
                 generalisation error and the empirical error. Despite
                 its solid theoretical foundation and reliability, SRM
                 has seldom been applied to GP. The most important
                 reason is the difficulty in measuring the VC dimension
                 of GP models/programs. This paper introduces SRM, which
                 is based on an empirical method to measure the VC
                 dimension of models, into GP to improve its
                 generalisation performance for symbolic regression. The
                 results of a set of experiments confirm that GP with
                 SRM has a dramatical generalisation gain while evolving
                 more compact/less complex models than standard GP.
                 Further analysis also shows that in most cases, GP with
                 SRM has better generalisation performance than GP with
                 bias-variance decomposition, which is one of the
                 state-of-the-art methods to control overfitting.",
  notes =        "Victoria University of Wellington

                 GECCO-2016 A Recombination of the 25th International
                 Conference on Genetic Algorithms (ICGA-2016) and the
                 21st Annual Genetic Programming Conference (GP-2016)",
}

Genetic Programming entries for Qi Chen Mengjie Zhang Bing Xue

Citations