Genetic Programming for Combining Neural Networks for Drug Discovery

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "W. B. Langdon and S. J. Barrett and B. F. Buxton",
  title =        "Genetic Programming for Combining Neural Networks for
                 Drug Discovery",
  booktitle =    "Soft Computing and Industry Recent Applications",
  year =         "2001",
  editor =       "Rajkumar Roy and Mario K{\"o}ppen and Seppo Ovaska and 
                 Takeshi Furuhashi and Frank Hoffmann",
  pages =        "597--608",
  month =        "10--24 " # sep,
  publisher =    "Springer-Verlag",
  note =         "Published 2002",
  keywords =     "genetic algorithms, genetic programming, data fusion,
                 data mining, knowledge discovery, Receiver Operating
                 Characteristics, ensemble of classifiers, size fair
  ISBN =         "1-85233-539-4",
  URL =          "",
  URL =          "",
  abstract =     "We have previously shown on a range of benchmarks
                 \cite{langdon:2001:gROC} Genetic programming (GP) can
                 automatically fuse given classifers of diverse types to
                 produce a combined classifer whose Receiver Operating
                 Characteristics (ROC) are better than
                 \cite{scott:1998:BMVC}'s 'Maximum Realisable Receiver
                 Operating Characteristics' (MRROC). I.e. better than
                 their convex hull. Here our technique is used in a
                 blind trial where artifcial neural networks ANN. are
                 trained by Clementine on P450 pharmaceutical data.
                 Using just the networks GP automatically evolves a
                 composite classifer.",
  notes =        "Out of print?


Genetic Programming entries for William B Langdon S J Barrett Bernard Buxton