PhysicsGP: A Genetic Programming Approach to Event Selection
Data Analysis, Statistics and Probability
2009-11-10 v1
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
Cite
@article{arxiv.physics/0402030,
title = {PhysicsGP: A Genetic Programming Approach to Event Selection},
author = {Kyle Cranmer and R. Sean Bowman},
journal= {arXiv preprint arXiv:physics/0402030},
year = {2009}
}
Comments
16 pages 9 figures, 1 table. Submitted to Comput. Phys. Commun