PhysicsGP: A genetic programming approach to event selection

Kyle Cranmer; R. Sean Bowman

doi:10.1016/j.cpc.2004.12.006

PhysicsGP: A genetic programming approach to event selection

Kyle Cranmer, R. Sean Bowman

Research output: Contribution to journal › Article › peer-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 language	English (US)
Pages (from-to)	165-176
Number of pages	12
Journal	Computer Physics Communications
Volume	167
Issue number	3
DOIs	https://doi.org/10.1016/j.cpc.2004.12.006
State	Published - May 1 2005

Keywords

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

ASJC Scopus subject areas

Hardware and Architecture
General Physics and Astronomy

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10.1016/j.cpc.2004.12.006

Cite this

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title = "PhysicsGP: A genetic programming approach to event selection",

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.",

keywords = "Classification, Genetic Programming, Genetic algorithms, Neural networks, Support vector machines, Triggering, VC dimension",

author = "Kyle Cranmer and Bowman, {R. Sean}",

note = "Funding Information: This work was supported by a graduate research fellowship from the National Science Foundation and US Department of Energy Grant DE-FG0295-ER40896. ",

year = "2005",

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doi = "10.1016/j.cpc.2004.12.006",

language = "English (US)",

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T2 - A genetic programming approach to event selection

AU - Cranmer, Kyle

AU - Bowman, R. Sean

N1 - Funding Information: This work was supported by a graduate research fellowship from the National Science Foundation and US Department of Energy Grant DE-FG0295-ER40896.

PY - 2005/5/1

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N2 - 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.

AB - 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.

KW - Classification

KW - Genetic Programming

KW - Genetic algorithms

KW - Neural networks

KW - Support vector machines

KW - Triggering

KW - VC dimension

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JF - Computer Physics Communications

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PhysicsGP: A genetic programming approach to event selection

Abstract

Keywords

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