PhysicsGP: A Genetic Programming Approach to Event Selection
arXiv:physics/0402030 · doi:10.1016/j.cpc.2004.12.006
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
16 pages 9 figures, 1 table. Submitted to Comput. Phys. Commun