Comparison between genetic programming and Neural Network in classification of buried unexploded ordnance (UXO) targets

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

@InProceedings{Kobashigawa:2010:APSURSI,
  author =       "Jill Kobashigawa and Hyoung-sun Youn and 
                 Magdy Iskander and Zhengqing Yun",
  title =        "Comparison between genetic programming and Neural
                 Network in classification of buried unexploded ordnance
                 (UXO) targets",
  booktitle =    "2010 IEEE Antennas and Propagation Society
                 International Symposium (APSURSI)",
  year =         "2010",
  month =        "11-17 " # jul,
  abstract =     "In this paper, we present the results of our next step
                 effort in comparison of classification performances
                 between the NN and the GP techniques based on the
                 simulated scattering patterns of UXO-like object and
                 non-UXO objects. For this comparative study, 2
                 dimensional scattering images from one UXO target and
                 four non-UXO objects were generated by numerical
                 simulation tool (FEKO). For non-UXO objects, the most
                 challenging targets to discriminate from UXO, since all
                 these objects produce resonance signal as UXO-like
                 targets do [6], were selected. Classification
                 performances of both techniques (NN vs. GP) in
                 different level of noise and in the case of presence of
                 untrained data were examined and the results and
                 observations are discussed.",
  keywords =     "genetic algorithms, genetic programming, buried
                 unexploded ordnance targets, dimensional scattering
                 images, ground penetrating radar, neural network,
                 numerical simulation, electrical engineering computing,
                 ground penetrating radar, neural nets, numerical
                 analysis",
  DOI =          "doi:10.1109/APS.2010.5561278",
  ISSN =         "1522-3965",
  notes =        "'we confirmed that GP provided better performance than
                 neural networks'.

                 Hawaii Center for Advanced Communications College of
                 Engineering, University of Hawaii at Manoa, USA 96822
                 Also known as \cite{5561278}",
}

Genetic Programming entries for Jill S K Nakatsu Hyoung-sun Youn Magdy F Iskander Zhengqing Yun

Citations