CasGP: Building Cascaded Hierarchical Models Using Niching

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{lichodzijewski:2005:CEC,
  author =       "Peter Lichodzijewski and Malcolm I. Heywood and 
                 A. Nur Zincir-Heywood",
  title =        "CasGP: Building Cascaded Hierarchical Models Using
                 Niching",
  booktitle =    "Proceedings of the 2005 IEEE Congress on Evolutionary
                 Computation",
  year =         "2005",
  editor =       "David Corne and Zbigniew Michalewicz and 
                 Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and 
                 Garrison Greenwood and Tan Kay Chen and 
                 Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and 
                 Jennifier Willies and Juan J. Merelo Guervos and 
                 Eugene Eberbach and Bob McKay and Alastair Channon and 
                 Ashutosh Tiwari and L. Gwenn Volkert and 
                 Dan Ashlock and Marc Schoenauer",
  volume =       "2",
  pages =        "1180--1187",
  address =      "Edinburgh, UK",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "2-5 " # sep,
  organisation = "IEEE Computational Intelligence Society, Institution
                 of Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, RSS, DSS,
                 C4.5, boosting, naive Bayes",
  ISBN =         "0-7803-9363-5",
  URL =          "http://flame.cs.dal.ca/~piotr/01554824.pdf",
  DOI =          "doi:10.1109/CEC.2005.1554824",
  size =         "8 pages",
  abstract =     "A Cascaded model is introduced for mining large
                 datasets using Genetic Programming without recourse to
                 specialist hardware. Such an algorithm satisfies the
                 seeming conflicting requirements of scalability and
                 accuracy on large datasets by incrementally building GP
                 classifiers through the use of a hierarchical Dynamic
                 Subset Selection algorithm. Models are built
                 incrementally with each layer of the cascade receiving
                 as input the original feature vector, plus the output
                 from the previous layer(s). In order to encourage each
                 layer to explicitly solve new aspects of the problem a
                 combination of Sum Square Error and Niching is used.
                 Thus, previous layers of the model are considered a
                 niche, and the cost function is a shared error
                 metric.",
  notes =        "CEC2005 - A joint meeting of the IEEE, the IEE, and
                 the EPS.",
}

Genetic Programming entries for Peter Lichodzijewski Malcolm Heywood Nur Zincir-Heywood

Citations