Created by W.Langdon from gp-bibliography.bib Revision:1.2031
@InProceedings{his02Plenary:Langdon,
pages = "6",
year = "2002",
title = "A Hybrid Genetic Programming Neural Network Classifier
for Use in Drug Discovery",
editor = "Ajith Abraham and Javier {Ruiz-del-Solar} and
Mario K{\"o}ppen",
chapter = "Abstracts of HIS02 Plenary Presentations",
series = "Frontiers in Artificial Intelligence and Applications
Vol. 87",
institution = "Department of Computer Science -- University College
London -- UK",
booktitle = "Soft Computing Systems - Design, Management and
Applications",
publisher = "IOS Press Amsterdam, Berlin, Oxford, Tokyo, Washington
D.C.",
author = "William B. Langdon",
month = "1-4 " # dec,
abstract = "We have shown genetic programming (GP) can
automatically fuse given classifiers of diverse types
to produce a hybrid classifier. Combinations of neural
networks, decision trees and Bayes classifier shave
been formed. On a range of benchmarks the evolved
multiple classifier system is better than all of its
components. Indeed its Receiver Operating
Characteristics (ROC) are better than [Scott et al.,
1998]s {"}Maximum Realisable Receiver Operating
Characteristics{"} MRROC (convex hull) An important
component in the drug discovery is testing potential
drugs for activity with P450 cell membrane molecules.
Our technique has been used in a blind trial where
artificial neural networks are trained by Clementine on
P450 pharmaceutical data. Using just the trained
networks, GP automatically evolves a composite
classifier. Recent experiments with boosting the
networks will be compared with genetic programming.",
note = "Invited conference speaker",
address = "Universidad de Chile, Chile",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-58603-297-6",
URL = "
http://www.cec.uchile.cl/~his02/index_files/abs_drug.pdf",
size = "1 page",
notes = "http://www.cec.uchile.cl/~his02/index.html old key
langdon:2002:his
ISBN? = 4 274 90558 6 C3055",
}
Genetic Programming entries for William B Langdon