Comparative study of genetic programming vs. neural networks for the classification of buried objects

Created by W.Langdon from gp-bibliography.bib Revision:1.4333

  author =       "Jill Kobashigawa and Hyoung-sun Youn and 
                 Magdy Iskander and Zhengqing Yun",
  title =        "Comparative study of genetic programming vs. neural
                 networks for the classification of buried objects",
  booktitle =    "IEEE Antennas and Propagation Society International
                 Symposium, APSURSI '09",
  year =         "2009",
  month =        jun,
  pages =        "1--4",
  keywords =     "genetic algorithms, genetic programming, buried
                 objects classification, character classification
                 problems, neural network structure optimization,
                 untrained data robustness, buried object detection,
                 image classification, neural nets",
  DOI =          "doi:10.1109/APS.2009.5172386",
  ISSN =         "1522-3965",
  abstract =     "A comparative study of neural networks and genetic
                 programming was conducted on six character
                 classification problems. Based on the obtained results
                 of the six problems, genetic programming showed better
                 performance than neural networks in the various levels
                 of problem difficulty. Genetic programming also showed
                 robustness to untrained data, which caused difficulties
                 for the neural networks. The optimization of the neural
                 network structure was observed to be integral in
                 obtaining both convergence and acceptable performance.
                 A clear trend for structure optimization is not evident
                 in the case of neural networks, and a global optimal
                 solution may not be practical. On the other hand,
                 because of the global searching nature of genetic
                 programming, these problems with neural networks could
                 be solved by using genetic programming.",
  notes =        "Also known as \cite{5172386}",

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