Genetic programming for stack filters

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InCollection{oakley:1997:HECsf,
  author =       "E. Howard N. Oakley",
  title =        "Genetic programming for stack filters",
  booktitle =    "Handbook of Evolutionary Computation",
  publisher =    "Oxford University Press",
  publisher_2 =  "Institute of Physics Publishing",
  year =         "1997",
  editor =       "Thomas Baeck and David B. Fogel and 
                 Zbigniew Michalewicz",
  chapter =      "section G3.1",
  keywords =     "genetic algorithms, genetic programming, FIR",
  ISBN =         "0-7503-0392-1",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf",
  DOI =          "doi:10.1201/9781420050387.ptg",
  size =         "5 pages",
  abstract =     "A range of techniques was used to search for the
                 fittest filter to remove noise from data from a blood
                 flow measurement system. Filter types considered
                 included finite impulse response (FIR), RC
                 (exponential), a generalized FIR form, and stack
                 filters. Techniques used to choose individual filters
                 were heuristic, the genetic algorithm, and genetic
                 programming. The efficacy of filters was assessed by
                 measuring a fitness function, derived from the root
                 mean square error. The fittest filter found was a stack
                 filter, generated by genetic programming. It
                 outperformed heuristically found median filters, and an
                 FIR filter first produced by the genetic algorithm and
                 then improved by genetic programming. Genetic
                 programming proved to be an inexpensive and effective
                 tool for the selection of an optimal filter from a
                 class of filters which is particularly difficult to
                 optimize. Its value in signal processing is confirmed
                 by its ability to further improve filters created by
                 other methods. Its main limitation is that it is, at
                 present, too computationally intensive to be used for
                 on-line adaptive filtering.",
  notes =        "blood flow",
}

Genetic Programming entries for Howard Oakley

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