Evolving Predictors for Chaotic Time Series

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  author =       "Peter J. Angeline",
  title =        "Evolving Predictors for Chaotic Time Series",
  booktitle =    "Proceedings of SPIE: Application and Science of
                 Computational Intelligence",
  year =         "1998",
  editor =       "S. Rogers and D. Fogel and J. Bezdek and B. Bosacchi",
  volume =       "3390",
  pages =        "170--80",
  publisher_address = "Bellingham, WA, USA",
  organisation = "SPIE",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 computation, evolutionary programming, neural networks,
                 chaotic time series prediction",
  URL =          "http://www.natural-selection.com/Library/1998/spie98.pdf",
  DOI =          "doi:10.1117/12.304803",
  size =         "11 pages",
  abstract =     "Neural networks are a popular representation for
                 inducing single-step predictors for chaotic times
                 series. For complex time series it is often the case
                 that a large number of hidden units must be used to
                 reliably acquire appropriate predictors. This paper
                 describes an evolutionary method that evolves a class
                 of dynamic systems with a form similar to neural
                 networks but requiring fewer computational units.
                 Results for experiments on two popular chaotic times
                 series are described and the current methods
                 performance is shown to compare favorably with using
                 larger neural networks.",

Genetic Programming entries for Peter John Angeline