Time Series Perturbation by Genetic Programming

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

@InProceedings{lee:2001:tspgp,
  author =       "G. Y. Lee",
  title =        "Time Series Perturbation by Genetic Programming",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "403--409",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, Time Series,
                 Perturbation Theory",
  ISBN =         "0-7803-6658-1",
  DOI =          "doi:10.1109/CEC.2001.934419",
  abstract =     "We present a new algorithm that combines perturbation
                 theory and genetic programming for modelling and
                 forecasting real-world chaotic time series. Both
                 perturbation theory and time series modeling have to
                 build symbolic models for very complex system dynamics.
                 Perturbation theory does not work without a
                 well-defined system equation. Difficulties in modelling
                 time series lie in the fact that we cannot have or
                 assume any system equation. The new algorithm shows how
                 genetic programming can be combined with perturbation
                 theory for time series modelling. Detailed discussions
                 on successful applications to chaotic time series from
                 practically important fields of science and engineering
                 are given. Computational resources were negligible as
                 compared with earlier similar regression studies based
                 on genetic programming. A desktop PC provides
                 sufficient computing power to make the new algorithm
                 very useful for real-world chaotic time series.
                 Especially, it worked very well for deterministic or
                 stationary time series, while stochastic or
                 nonstationary time series needed extended effort, as it
                 should be",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number =",
}

Genetic Programming entries for G Y Lee

Citations