PhysicsGP: A Genetic Programming approach to event selection

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

@Article{cranmer:2005:CPC,
  author =       "Kyle Cranmer and R. Sean Bowman",
  title =        "{PhysicsGP}: A Genetic Programming approach to event
                 selection",
  journal =      "Computer Physics Communications",
  year =         "2005",
  volume =       "167",
  number =       "3",
  pages =        "165--176",
  month =        "1 " # may,
  keywords =     "genetic algorithms, genetic programming, Triggering,
                 Classification, VC dimension, Genetic algorithms,
                 Neural networks, Support vector machines",
  ISSN =         "0010-4655",
  URL =          "http://arxiv.org/abs/physics/0402030",
  DOI =          "doi:10.1016/j.cpc.2004.12.006",
  abstract =     "We present a novel multivariate classification
                 technique based on Genetic Programming. The technique
                 is distinct from Genetic Algorithms and offers several
                 advantages compared to Neural Networks and Support
                 Vector Machines. The technique optimises a set of
                 human-readable classifiers with respect to some
                 user-defined performance measure. We calculate the
                 Vapnik-Chervonenkis dimension of this class of learning
                 machines and consider a practical example: the search
                 for the Standard Model Higgs Boson at the LHC. The
                 resulting classifier is very fast to evaluate,
                 human-readable, and easily portable. The software may
                 be downloaded at:
                 http://cern.ch/~cranmer/PhysicsGP.html.",
  notes =        "replaces oai:arXiv.org:physics/0402030
                 http://www.elsevier.com/wps/find/journaldescription.cws_home/505710/description#description

                 p171 {"}It is meaningless to calculate the VCD
                 (Vapnik-Chervonenkis dimension) for GP in general...{"}
                 {"}by placing a bound on either size... or the degree
                 of the polynomial, we can calculate a sensible
                 VCD.{"}

                 GP compared with ANN (backprop + momentum) and SVM with
                 RBF kernel (BSVM-2.0). Training data subsampled.

                 p174 {"}GP approach does not seem particularly
                 sensitive to the size penalty of mutation rates{"}.",
}

Genetic Programming entries for Kyle S Cranmer R Sean Bowman

Citations