Visual Learning by Coevolutionary Feature Synthesis

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Krzysztof Krawiec and Bir Bhanu",
  title =        "Visual Learning by Coevolutionary Feature Synthesis",
  journal =      "{IEEE} Transactions on System, Man, and Cybernetics --
                 Part B",
  number =       "3",
  pages =        "409--425",
  volume =       "35",
  month =        jun,
  year =         "2005",
  keywords =     "genetic algorithms, genetic programming, Automatic
                 programming, feature extraction, genetic algorithms,
                 pattern recognition",
  URL =          "",
  DOI =          "doi:10.1109/TSMCB.2005.846644",
  size =         "17 pages",
  abstract =     "A novel genetically inspired visual learning method is
                 proposed. Given the training raster images, this
                 general approach induces a sophisticated feature-based
                 recognition system. It employs the paradigm of
                 cooperative coevolution to handle the computational
                 difficulty of this task. To represent the feature
                 extraction agents, the linear genetic programming is
                 used. The paper describes the learning algorithm and
                 provides a firm rationale for its design. Different
                 architectures of recognition systems are considered
                 that employ the proposed feature synthesis method. An
                 extensive experimental evaluation on the demanding
                 real-world task of object recognition in synthetic
                 aperture radar (SAR) imagery shows the ability of the
                 proposed approach to attain high recognition
                 performance in different operating conditions.",

Genetic Programming entries for Krzysztof Krawiec Bir Bhanu