Multiobjective Genetic Programming Feature Extraction with Optimized Dimensionality

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Yang Zhang and Peter I. Rockett",
  title =        "Multiobjective Genetic Programming Feature Extraction
                 with Optimized Dimensionality",
  booktitle =    "Soft Computing in Industrial Applications",
  publisher =    "Springer",
  year =         "2006",
  editor =       "Ashraf Saad and Erel Avineri and Keshav Dahal and 
                 Muhammad Sarfraz and Rajkumar Roy",
  volume =       "39",
  series =       "Advances in Soft Computing",
  pages =        "159--168",
  month =        "18 " # sep # " - 6 " # oct,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  DOI =          "doi:10.1007/978-3-540-70706-6",
  abstract =     "We present a multi-dimensional mapping strategy using
                 multiobjective genetic programming (MOGP) to search for
                 the (near-)optimal feature extraction pre-processing
                 stages for pattern classification as well as optimizing
                 the dimensionality of the decision space. We search for
                 the set of mappings with optimal dimensionality to
                 project the input space into a decision space with
                 maximized class separability. The steady-state Pareto
                 converging genetic programming (PCGP) has been used to
                 implement this multi-dimensional MOGP. We examine the
                 proposed method using eight benchmark datasets from the
                 UCI database and the Statlog project to make
                 quantitative comparison with conventional classifiers.
                 We conclude that MMOGP outperforms the comparator
                 classifiers due to its optimized feature extraction
  notes =        "WSC11 2006 published 2007",
  size =         "10 pages",

Genetic Programming entries for Yang Zhang Peter I Rockett