Empirical Analysis of Schemata in Genetic Programming using Maximal Schemata and MSG
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Smart:2008:cec,
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author = "Will Smart and Mengjie Zhang",
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title = "Empirical Analysis of Schemata in Genetic Programming
using Maximal Schemata and {MSG}",
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booktitle = "2008 IEEE World Congress on Computational
Intelligence",
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year = "2008",
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editor = "Jun Wang",
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pages = "2983--2990",
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address = "Hong Kong",
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month = "1-6 " # jun,
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organization = "IEEE Computational Intelligence Society",
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publisher = "IEEE Press",
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isbn13 = "978-1-4244-1823-7",
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file = "EC0665.pdf",
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DOI = "doi:10.1109/CEC.2008.4631200",
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abstract = "Plenteous research studies schemata in Genetic
Programming (GP), though little of it is been
empirical, due to the vast numbers of typical schemata
in even small populations. In this research, we define
maximal schemata, and extend our Trips algorithm to the
more general Max-Schema-Growth (MSG) algorithm,
applicable to a wider range of schema forms (Trips only
handles standard fragment schemata). We present MSG
specialised to work with unordered-fragments schemata
(tree-fragments with unordered functions), and compare
the number of maximal schemata found of these two
forms. For most maximal fragments, another maximal
fragment was also found that differed only by the
orders of function node arguments. We conclude that
maximal unordered-fragments may represent a greater
range of common patterns between programs than standard
maximal fragments, though the greater reach comes at a
price with a severe increase in the time taken by the
algorithm.",
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keywords = "genetic algorithms, genetic programming",
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notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
EPS and the IET.",
- }
Genetic Programming entries for
Will Smart
Mengjie Zhang
Citations