Genetic Programming and its application to HEP

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

  author =       "Erik Vandering",
  title =        "Genetic Programming and its application to {HEP}",
  booktitle =    "Computing in High Energy Physics, CHEP'04",
  year =         "2004",
  editor =       "J. Harvey",
  address =      "Interlaken, Switzerland",
  month =        "27 " # sep # "-1 " # oct,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  URL =          "",
  abstract =     "Genetic programming is a machine learning technique,
                 popularized by Koza in 1992, in which computer programs
                 which solve user-posed problems are automatically
                 discovered. Populations of programs are evaluated for
                 their fitness of solving a particular problem. New
                 populations of ever increasing fitness are generated by
                 mimicking the biological processes underlying
                 evolution. These processes are principally genetic
                 recombination, mutation, and survival of the fittest.
                 Genetic programming has potential advantages over other
                 machine learning techniques such as neural networks and
                 genetic algorithms in that the form of the solution is
                 not specified in advance and the program can grow as
                 large as necessary to adequately solve the posed

                 This talk will give an overview and demonstration of
                 the genetic programming technique and show a successful
                 application in high energy physics: the automatic
                 construction of an event filter for FOCUS which is more
                 powerful than the experiment's usual methods of event
                 selection. We have applied this method to the study of
                 doubly Cabibbo suppressed decays of charmed hadrons
                 ($D^+$, $D_s^+$, and $\Lambda_c^+$).",
  notes =        "Tutorial 0.pdf are presentation


                 VANDERBILT UNIVERSITY",

Genetic Programming entries for Erik Vandering