Recognizing patterns in protein sequences using iteration-performing calculations in genetic programming

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

@InProceedings{Koza:1994:rppsGP,
  author =       "J. R. Koza",
  title =        "Recognizing patterns in protein sequences using
                 {iteration-performing} calculations in genetic
                 programming",
  booktitle =    "1994 IEEE World Congress on Computational
                 Intelligence",
  year =         "1994",
  volume =       "1",
  pages =        "244--249",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, memory",
  URL =          "http://www.genetic-programming.com/jkpdf/icec1994.pdf",
  size =         "7 pages",
  abstract =     "This paper uses genetic programming with automatically
                 defined functions (ADFs) for the dynamic creation of a
                 pattern-recognising computer program consisting of
                 initially-unknown detectors, an initially-unknown
                 iterative calculation incorporating the
                 as-yet-undiscovered detectors, and an
                 initially-unspecified final calculation incorporating
                 the results of the as-yet-unspecified iteration. The
                 program's goal is to recognise a given protein segment
                 as being a transmembrane domain or non-transmembrane
                 area of the protein. Genetic programming with automatic
                 function definition is given a training set of
                 differently-sized mouse protein segments and their
                 correct classification. Correlation is used as the
                 fitness measure. Automatic function definition enables
                 genetic programming to dynamically create subroutines
                 (detectors). A restricted form of iteration is
                 introduced to enable genetic programming to perform
                 calculations on the values returned by the detectors.
                 When cross-validated, the best genetically-evolved
                 recogniser for transmembrane domains achieves an
                 out-of-sample correlation of 0.968 and an out-of-sample
                 error rate of 1.6percent. This error rate is better
                 than that recently reported for five other methods.",
}

Genetic Programming entries for John Koza

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