Designing a classifier by a layered multi-population genetic programming approach

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Lin20072211,
  author =       "Jung-Yi Lin and Hao-Ren Ke and Been-Chian Chien and 
                 Wei-Pang Yang",
  title =        "Designing a classifier by a layered multi-population
                 genetic programming approach",
  journal =      "Pattern Recognition",
  volume =       "40",
  number =       "8",
  pages =        "2211--2225",
  year =         "2007",
  note =         "Part Special Issue on Visual Information Processing",
  ISSN =         "0031-3203",
  DOI =          "DOI:10.1016/j.patcog.2007.01.003",
  URL =          "http://www.sciencedirect.com/science/article/B6V14-4MVVSM4-5/2/2085e138e1b34ae21d5e76438ae3fc70",
  keywords =     "genetic algorithms, genetic programming,
                 Classification, Evolutionary computation,
                 Multi-population genetic programming",
  abstract =     "This paper proposes a method called layered genetic
                 programming (LAGEP) to construct a classifier based on
                 multi-population genetic programming (MGP). LAGEP
                 employs layer architecture to arrange multiple
                 populations. A layer is composed of a number of
                 populations. The results of populations are
                 discriminant functions. These functions transform the
                 training set to construct a new training set. The
                 successive layer uses the new training set to obtain
                 better discriminant functions. Moreover, because the
                 functions generated by each layer will be composed to a
                 long discriminant function, which is the result of
                 LAGEP, every layer can evolve with short individuals.
                 For each population, we propose an adaptive mutation
                 rate tuning method to increase the mutation rate based
                 on fitness values and remaining generations. Several
                 experiments are conducted with different settings of
                 LAGEP and several real-world medical problems.
                 Experiment results show that LAGEP achieves comparable
                 accuracy to single population GP in much less time.",
}

Genetic Programming entries for Mick Jung-Yi Lin Hao-Ren Ke Been-Chian Chien Wei-Pang Yang

Citations