Genetic Programming for Improved Data Mining: An Application to the Biochemistry of Protein Interactions
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{raymer:1996:GPidm:bpi,
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author = "M. L. Raymer and W. F. Punch and E. D. Goodman and
L. A. Kuhn",
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title = "Genetic Programming for Improved Data Mining: An
Application to the Biochemistry of Protein
Interactions",
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booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
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editor = "John R. Koza and David E. Goldberg and
David B. Fogel and Rick L. Riolo",
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year = "1996",
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month = "28--31 " # jul,
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keywords = "genetic algorithms, genetic programming",
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pages = "375--380",
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address = "Stanford University, CA, USA",
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publisher = "MIT Press",
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URL = "http://garage.cse.msu.edu/papers/GARAGe96-04-01.pdf",
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URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap51.pdf",
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URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
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size = "6 pages",
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abstract = "We have previously shown how a genetic algorithm (GA)
can be used to perform `data mining' the discovery of
particular/important data within large datasets, by
finding optimal data classifications using known
examples. However, these approaches, while successful,
limited data relationships to those that were `fixed'
before the GA run. We report here on an extension of
our previous work, substituting a genetic program (GP)
for a GA. The GP could optimise data classification, as
did the GA, but could also determine the functional
relationships among the features. This gave improved
performance and new information on important
relationships among features. We discuss the overall
approach, and compare the effectiveness of the GA vs.
GP on a biochemistry problem, the determination of the
involvement of bound water molecules in protein
interactions.",
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notes = "GP-96 Also available as TR GARAGe96-04-01",
- }
Genetic Programming entries for
Michael L Raymer
William F Punch
Erik Goodman
L A Kuhn
Citations