PhysicsGP: A genetic programming approach to event selection

Kyle Cranmer, R. Sean Bowman

    Research output: Contribution to journalArticlepeer-review

    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.

    Original languageEnglish (US)
    Pages (from-to)165-176
    Number of pages12
    JournalComputer Physics Communications
    Volume167
    Issue number3
    DOIs
    StatePublished - May 1 2005

    Keywords

    • Classification
    • Genetic Programming
    • Genetic algorithms
    • Neural networks
    • Support vector machines
    • Triggering
    • VC dimension

    ASJC Scopus subject areas

    • Hardware and Architecture
    • Physics and Astronomy(all)

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