Evolutionary Artificial Neural Networks and Genetic Programming: A Comparative Study Based on Financial Data

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

@InProceedings{chen:1997:eannGPfd,
  author =       "Shu-Heng Chen and Chih-Chi Ni",
  title =        "Evolutionary Artificial Neural Networks and Genetic
                 Programming: A Comparative Study Based on Financial
                 Data",
  booktitle =    "Artificial Neural Nets and Genetic Algorithms:
                 Proceedings of the International Conference,
                 ICANNGA97",
  year =         "1997",
  editor =       "George D. Smith and Nigel C. Steele and 
                 Rudolf F. Albrecht",
  pages =        "397--400",
  address =      "University of East Anglia, Norwich, UK",
  publisher =    "Springer-Verlag",
  note =         "published in 1998",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-211-83087-1",
  DOI =          "doi:10.1007/978-3-7091-6492-1_87",
  abstract =     "In this paper, the stock index S&P 500 is used to test
                 the predicting performance of genetic programming (GP)
                 and genetic programming neural networks (GPNN). While
                 both GP and GPNN are considered universal
                 approximators, in this practical financial application,
                 they perform differently. GPNN seemed to suffer the
                 overlearning (over fitting) problem more seriously than
                 GP; the latter outdid the former in all the
                 simulations.",
  notes =        "http://www.sys.uea.ac.uk/Research/ResGroups/MAG/ICANNGA97/papers_frame.html

                 Opps duplicates chen:1997:eANNGP Note: 22 Aug 2004
                 chen:1997:eANNGP combined with
                 \cite{chen:1997:eannGPfd}",
}

Genetic Programming entries for Shu-Heng Chen Chih-Chi Ni

Citations