Recognizing patterns in protein sequences using iteration-performing calculations in genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Koza:1994:rppsGP,
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author = "J. R. Koza",
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title = "Recognizing patterns in protein sequences using
{iteration-performing} calculations in genetic
programming",
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booktitle = "1994 IEEE World Congress on Computational
Intelligence",
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year = "1994",
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volume = "1",
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pages = "244--249",
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address = "Orlando, Florida, USA",
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month = "27-29 " # jun,
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming, memory",
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URL = "http://www.genetic-programming.com/jkpdf/icec1994.pdf",
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DOI = "doi:10.1109/ICEC.1994.350008",
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size = "7 pages",
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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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