Two Scientific Applications of Genetic Programming: Stack Filters and Non-Linear Equation Fitting to Chaotic Data

Created by W.Langdon from gp-bibliography.bib Revision:1.4128

@InCollection{kinnear:oakley,
  author =       "Howard Oakley",
  title =        "Two Scientific Applications of Genetic Programming:
                 Stack Filters and Non-Linear Equation Fitting to
                 Chaotic Data",
  booktitle =    "Advances in Genetic Programming",
  publisher =    "MIT Press",
  editor =       "Kenneth E. {Kinnear, Jr.}",
  year =         "1994",
  pages =        "369--389",
  chapter =      "17",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.amazon.co.uk/Advances-Genetic-Programming-Complex-Adaptive/dp/0262111888",
  URL =          "http://cognet.mit.edu/sites/default/files/books/9780262277181/pdfs/9780262277181_chap17.pdf",
  size =         "21 pages",
  abstract =     "Optimisation within nearly infinite search space is a
                 common problem in applied science, for which two
                 examples illustrate the application of genetic
                 programming. Three different techniques were used to
                 develop filters for the removal of noise from
                 experimental data. Heuristic search was used to develop
                 a median filter, a classical genetic algorithm
                 optimized a 7-tap moving average (FIR) filter, and
                 genetic programming was used to optimize a stack
                 filter. The latter had the highest fitness, and was
                 computationally more efficient than the best median
                 filter, which was in turn superior in fitness to the
                 best moving average filter. Genetic programming was
                 also used to fit empirical equations to a chaotic
                 time-series (the Mackey-Glass equation) and non-linear
                 physiological data. Initial results confirm the key
                 role of the fitness measure in such work; oscillatory
                 series are readily fitted with linear functions unless
                 the computation of fitness includes an appropriate
                 measure such as incremental comparison of Fourier power
                 series. The use of Lyapunov exponents and dimension
                 estimation is suggested in more sophisticated compound
                 fitness measures. Genetic programming may prove to be
                 useful in both forecasting and structural studies of
                 non-linear systems, at both local and global levels.",
  notes =        "Two Scientific Applications of Genetic Programming:
                 The development of stack filters, the fitting of
                 non-linear equations to chaotic data

                 Mackey-Glass, REAL World examples. Contrasts with other
                 techniques eg GA.

                 ",
}

Genetic Programming entries for Howard Oakley

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