Performance improvement of machine learning via automatic discovery of facilitating functions as applied to a problem of symbolic system identification
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- @InProceedings{Koza:1993:pimlssi,
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author = "John R. Koza and Martin A. Keane and James P. Rice",
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title = "Performance improvement of machine learning via
automatic discovery of facilitating functions as
applied to a problem of symbolic system
identification",
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booktitle = "1993 IEEE International Conference on Neural
Networks",
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year = "1993",
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volume = "I",
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pages = "191--198",
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address = "San Francisco, USA",
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publisher_address = "Piscataway, NJ, USA",
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming",
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URL = "http://www.genetic-programming.com/jkpdf/icnn1993impulse.pdf",
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size = "8 pages",
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abstract = "The recently developed genetic programming paradigm
provides a way to genetically breed a population of
computer programs to solve problems. Automatic function
definition enables genetic programming to define
potentially useful functions dynamically during a run -
much as a human programmer writing a complex computer
program creates subroutines to perform certain groups
of steps which must be performed in more than one place
in the main program. This paper illustrates the value
of automatic function definition in enabling genetic
programming to accelerate the finding of an
approximation to the impulse response function, in
symbolic form, for a linear time-invariant system.",
- }
Genetic Programming entries for
John Koza
Martin A Keane
James P Rice
Citations