The application of genetic programming to the investigation of short, noisy, chaotic data series

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "E. Howard N. Oakley",
  title =        "The application of genetic programming to the
                 investigation of short, noisy, chaotic data series",
  booktitle =    "Evolutionary Computing, AISB workshop",
  publisher =    "Springer-Verlag",
  year =         "1994",
  editor =       "T. C. Fogarty",
  volume =       "865",
  series =       "Lecture Notes in Computer Science",
  pages =        "320--332",
  address =      "Leeds, UK",
  month =        "11-13 " # apr,
  organisation = "AISB",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-58483-8",
  broken =       "",
  DOI =          "DOI:10.1007/3-540-58483-8_24",
  size =         "10 pages",
  abstract =     "Techniques to investigate chaotic data require long
                 noise-free series. Genetic programming allows fitting
                 of arbitrary functions to short noisy datasets.
                 Conventional genetic programming was used to fit Lisp
                 S-expressions to a known chaotic series (the
                 Mackey-Glass equation, discretized to a map) with added
                 noise. Embedding was performed by including previous
                 values in time in the terminal set. Prediction
                 intervals were 20--1065 steps into the future, based
                 upon near-minimal 35 training points from the

                 Fittest S-expressions yielded useful structural
                 information. Semilogarithmic plots of normalised root
                 mean squared error of the fittest forecasts against the
                 length of forecast showed two dominant slopes. Noise
                 led to a small exponential increase in this error.
                 Genetic programming appears useful, as it compares
                 favourably with established techniques, is robust to
                 noise, and easily avoids overfitting.",
  notes =        "Proceedings of the Workshop on Artificial Intelligence
                 and Simulation of Behaviour Workshop on Evolutionary
                 Computing. Workshop in Leeds, UK, April 11-13,

                 OAKPAPRS.TARR.GZ contains: AISBLNCS.TEX - TeX format,
                 but without the illustrations. If you want to see the
                 pictures, then you must get hold of a copy of the

Genetic Programming entries for Howard Oakley