- title = "Physics{GP}: {A} Genetic Programming Approach to Event Selection",
- author = "Kyle Cranmer and R. Sean Bowman",
- year = "2004",
- month = feb # "~05",
- 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",
- note = "Comment: 16 pages 9 figures, 1 table. Submitted to Comput. Phys. Commun",
- oai = "oai:arXiv.org:physics/0402030",
-
URL = "
http://arXiv.org/abs/physics/0402030",
- keywords = "genetic algorithms, genetic programming, Triggering, Classification, VC Dimension, Neural Networks, Support Vector Machines",
- size = "pages",
-
notes = "Published as \cite{cranmer:2005:CPC}.
cites \cite{luke:2000:2ftcaGP}.
Population is converted to C and compiled for fitness evaluation. (Details of GP including fitness definition (Gaussian/Poisson significance?) are vague). selection pressure based on inverse cumulative fitness distribution. Recentered fitness?
Ring topology CORBA parallel programming. Island model.
Higgs Boson, Large Hadron Collider CERN.",
