Performance improvement of machine learning via automatic discovery of facilitating functions as applied to a problem of symbolic system identification

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Koza:1993:pimlssi,

• author = "John R. Koza and Martin A. Keane and James P. Rice",
• title = "Performance improvement of machine learning via automatic discovery of facilitating functions as applied to a problem of symbolic system identification",
• booktitle = "1993 IEEE International Conference on Neural Networks",
• year = "1993",
• volume = "I",
• pages = "191--198",
• address = "San Francisco, USA",
• publisher_address = "Piscataway, NJ, USA",
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming",
• URL = "http://www.genetic-programming.com/jkpdf/icnn1993impulse.pdf",
• size = "8 pages",
• 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

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