Dynamics of Genetic Programming and Chaotic Time Series Prediction

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Brian S. Mulloy and Rick L. Riolo and 
                 Robert S. Savit",
  title =        "Dynamics of Genetic Programming and Chaotic Time
                 Series Prediction",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and 
                 David B. Fogel and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  pages =        "166--174",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "http://www.pscs.umich.edu/reprints/pscs-96-001.ps.gz",
  URL =          "http://citeseer.ist.psu.edu/517385.html",
  size =         "9 pages",
  abstract =     "An investigation into the dynamics of Genetic
                 Programming applied to chaotic time series prediction
                 is reported. An interesting characteristic of adaptive
                 search techniques is their ability to perform well in
                 many problem domains while failing in others. Because
                 of Genetic Programming's flexible tree structure, any
                 particular problem can be represented in myriad forms.
                 These representations have variegated effects on search
                 performance. Therefore, an aspect of fundamental
                 engineering significance is to find a representation
                 which, when acted upon by Genetic Programming
                 operators, optimizes search performance. We discover,
                 in the case of chaotic time series prediction, that the
                 representation commonly used in this domain does not
                 yield optimal solutions. Instead, we find that the
                 population converges onto one ``accurately
                 replicating'' tree before other trees can be explored.
                 To correct for this premature convergence we make a
                 simple modification to the crossover operator. In this
                 paper we review previous work with GP time series
                 prediction, pointing out an anomalous result related to
                 overlearning, and report the improvement effected by
                 our modified crossover operator.",
  URL =          "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap20.pdf",
  URL =          "http://cognet.mit.edu/library/books/view?isbn=0262611279",
  notes =        "GP-96",

Genetic Programming entries for Brian S Mulloy Rick L Riolo Robert S Savit