Hybrid Genetic Programming and GMDH System: STROGANOFF
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- @InCollection{Iba:2009:GPGMDH,
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author = "Hitoshi Iba",
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title = "Hybrid Genetic Programming and {GMDH} System:
{STROGANOFF}",
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booktitle = "Hybrid Self-Organizing Modeling Systems",
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publisher = "Springer",
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year = "2009",
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editor = "Godfrey C. Onwubolu",
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volume = "211",
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series = "Studies in Computational Intelligence",
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pages = "27--98",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-642-01530-4",
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URL = "https://doi.org/10.1007/978-3-642-01530-4_2",
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DOI = "doi:10.1007/978-3-642-01530-4_2",
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abstract = "This chapter introduces a new approach to Genetic
Programming (GP), based on GMDH-based technique, which
integrates a GP-based adaptive search of tree
structures, and a local parameter tuning mechanism
employing statistical search. The GP is supplemented
with a local hill climbing search, using a parameter
tuning procedure. More precisely, we integrate the
structural search of traditional GP with a multiple
regression analysis method and establish our adaptive
program called STROGANOFF (i.e. STructured
Representation On Genetic Algorithms for NOnlinear
Function Fitting). The fitness evaluation is based on a
Minimum Description Length (MDL) criterion, which
effectively controls the tree growth in GP. Its
effectiveness is demonstrated by solving several system
identification (numerical) problems and comparing the
performance of STROGANOFF with traditional GP and
another standard technique. The effectiveness of this
numerical approach to GP is demonstrated by successful
application to computational finances.",
- }
Genetic Programming entries for
Hitoshi Iba
Citations