Fitness enhancement of layered architecture genetic programming

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@InProceedings{Lin:2010:ICS,
  author =       "Jung Yi Lin",
  title =        "Fitness enhancement of layered architecture genetic
                 programming",
  booktitle =    "2010 International Computer Symposium (ICS)",
  year =         "2010",
  month =        "16-18 " # dec,
  pages =        "700--704",
  abstract =     "Layered architecture genetic programming (LAGEP) has
                 been applied on variety classification problems. It
                 organises populations as layers. Populations in
                 different layers evolve with different training sets.
                 Individuals produced by populations of layer Li
                 transform training instances into new ones. Populations
                 in Li+1 then evolve with the new training set instead
                 of evolve with the original given training set. Each
                 population in Li produces one feature for the new
                 training instances. New training instances could have
                 fewer features and are easier to be classified. Such
                 mechanism makes consecutive layer gain better fitness
                 value than preceding layers do. At this paper, we
                 intend to analyse the enhancement of fitness value over
                 all layers. We conduct experiments with a
                 high-dimensional gene expression dataset to show the
                 fitness enhancement.",
  keywords =     "genetic algorithms, genetic programming,
                 classification problems, fitness enhancement, high
                 dimensional gene expression dataset, layered
                 architecture genetic programming, pattern
                 classification",
  DOI =          "doi:10.1109/COMPSYM.2010.5685423",
  notes =        "Also known as \cite{5685423}",
}

Genetic Programming entries for Mick Jung-Yi Lin

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