- 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",
