- 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 = "
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}",
