- author = "Erik Vandering",
- title = "Genetic Programming and its application to {HEP}",
- booktitle = "Computing in High Energy Physics, CHEP'04",
- year = "2004",
- editor = "J. Harvey",
- address = "Interlaken, Switzerland",
- month = "27 " # sep # "-1 " # oct,
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://uscms-docdb.fnal.gov/cgi-bin/ShowDocument?docid=1848",
-
URL = "
http://indico.cern.ch/event/0/session/4/contribution/49/material/slides/0.pdf",
-
abstract = "Genetic programming is a machine learning technique,
popularized by Koza in 1992, in which computer programs
which solve user-posed problems are automatically
discovered. Populations of programs are evaluated for
their fitness of solving a particular problem. New
populations of ever increasing fitness are generated by
mimicking the biological processes underlying
evolution. These processes are principally genetic
recombination, mutation, and survival of the fittest.
Genetic programming has potential advantages over other
machine learning techniques such as neural networks and
genetic algorithms in that the form of the solution is
not specified in advance and the program can grow as
large as necessary to adequately solve the posed
problem.
This talk will give an overview and demonstration of the genetic programming technique and show a successful application in high energy physics: the automatic construction of an event filter for FOCUS which is more powerful than the experiment's usual methods of event selection. We have applied this method to the study of doubly Cabibbo suppressed decays of charmed hadrons ($D^+$, $D_s^+$, and $\Lambda_c^+$).",
-
notes = "Tutorial 0.pdf are presentation
slides.
http://chep2004.web.cern.ch/chep2004/
VANDERBILT UNIVERSITY",
