Hybrid evolutionary learning for synthesizing multi-class pattern recognition systems

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

@Article{Zmuda:2003:ASC,
  author =       "Michael A. Zmuda and Mateen M. Rizki and 
                 Louis A. Tamburino",
  title =        "Hybrid evolutionary learning for synthesizing
                 multi-class pattern recognition systems",
  journal =      "Applied Soft Computing",
  year =         "2003",
  volume =       "2",
  pages =        "269--282",
  number =       "4",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 computation, Evolutionary programming, Hybrid
                 evolutionary algorithm, Pattern recognition,
                 Classification",
  owner =        "wlangdon",
  URL =          "http://www.sciencedirect.com/science/article/B6W86-47DT5VK-2/2/2d9804998b4546170ea1fb3a60909666",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/S1568-4946(02)00060-1",
  abstract =     "This paper describes one aspect of a machine-learning
                 system called HELPR that blends the best aspects of
                 different evolutionary techniques to bootstrap-up a
                 complete recognition system from primitive input data.
                 HELPR uses a multi-faceted representation consisting of
                 a growing sequence of non-linear mathematical
                 expressions. Individual features are represented as
                 tree structures and manipulated using the techniques of
                 genetic programming. Sets of features are represented
                 as list structures that are manipulated using genetic
                 algorithms and evolutionary programming. Complete
                 recognition systems are formed in this version of HELPR
                 by attaching the evolved features to multiple
                 perceptron discriminators. Experiments on datasets from
                 the University of California at Irvine (UCI)
                 machine-learning repository show that HELPR's
                 performance meets or exceeds accuracies previously
                 published.",
}

Genetic Programming entries for Michael A Zmuda Mateen M Rizki Louis A Tamburino

Citations