%0 Journal Article %1 cranmer:2005:CPC %A Cranmer, Kyle %A Bowman, R. Sean %D 2005 %J Computer Physics Communications %K Classification, Genetic Neural Support Triggering, VC algorithms, dimension, genetic machines networks, programming, vector %N 3 %P 165--176 %R doi:10.1016/j.cpc.2004.12.006 %T PhysicsGP: A Genetic Programming approach to event selection %V 167 %X 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.

@article{cranmer:2005:CPC, 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.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Cranmer, Kyle and Bowman, R. Sean}, biburl = {https://www.bibsonomy.org/bibtex/22147cbfd001c12ba19943d268baa57e3/brazovayeye}, doi = {doi:10.1016/j.cpc.2004.12.006}, interhash = {28a3f537edd229b97c04ab501d81537b}, intrahash = {2147cbfd001c12ba19943d268baa57e3}, issn = {0010-4655}, journal = {Computer Physics Communications}, keywords = {Classification, Genetic Neural Support Triggering, VC algorithms, dimension, genetic machines networks, programming, vector}, month = {1 May}, 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{"}.}, number = 3, pages = {165--176}, timestamp = {2008-06-19T17:38:13.000+0200}, title = {{PhysicsGP}: {A} Genetic Programming approach to event selection}, volume = 167, year = 2005 }