PhysicsGP: A Genetic Programming Approach to Event Selection

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

@Misc{oai:arXiv.org:physics/0402030,
  title =        "Physics{GP}: {A} Genetic Programming Approach to Event
                 Selection",
  author =       "Kyle Cranmer and R. Sean Bowman",
  year =         "2004",
  month =        feb # "~05",
  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 optimizes 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",
  note =         "Comment: 16 pages 9 figures, 1 table. Submitted to
                 Comput. Phys. Commun",
  oai =          "oai:arXiv.org:physics/0402030",
  URL =          "http://arXiv.org/abs/physics/0402030",
  keywords =     "genetic algorithms, genetic programming, Triggering,
                 Classification, VC Dimension, Neural Networks, Support
                 Vector Machines",
  size =         "pages",
  notes =        "Published as \cite{cranmer:2005:CPC}.

                 cites \cite{luke:2000:2ftcaGP}.

                 Population is converted to C and compiled for fitness
                 evaluation. (Details of GP including fitness definition
                 (Gaussian/Poisson significance?) are vague). selection
                 pressure based on inverse cumulative fitness
                 distribution. Recentered fitness?

                 Ring topology CORBA parallel programming. Island
                 model.

                 Higgs Boson, Large Hadron Collider CERN.",
}

Genetic Programming entries for Kyle S Cranmer R Sean Bowman

Citations