A Grammar-guided Genetic Programming Framework Configured for Data Mining and Software Testing
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gp-bibliography.bib Revision:1.8098
- @Article{journals/ijseke/VergilioP06,
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title = "A Grammar-guided Genetic Programming Framework
Configured for Data Mining and Software Testing",
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author = "Silvia Regina Vergilio and
Aurora Trinidad Ramirez Pozo",
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journal = "International Journal of Software Engineering and
Knowledge Engineering",
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year = "2006",
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number = "2",
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volume = "16",
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pages = "245--268",
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bibdate = "2006-05-22",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/ijseke/ijseke16.html#VergilioP06",
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keywords = "genetic algorithms, genetic programming, Evolutionary
computation, data mining, software testing, grammars",
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DOI = "doi:10.1142/S0218194006002781",
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abstract = "Genetic Programming (GP) is a powerful software
induction technique that can be applied to solve a wide
variety of problems. However, most researchers develop
tailor-made GP tools for solving specific problems.
These tools generally require significant modifications
in their kernel to be adapted to other domains. In this
paper, we explore the Grammar-Guided Genetic
Programming (GGGP) approach as an alternative to
overcome such limitation. We describe a GGGP based
framework, named Chameleon, that can be easily
configured to solve different problems. We explore the
use of Chameleon in two domains, not usually addressed
by works in the literature: in the task of mining
relational databases and in the software testing
activity. The presented results point out that the use
of the grammar-guided approach helps us to obtain more
generic GP frameworks and that they can contribute in
the explored domains.",
- }
Genetic Programming entries for
Silvia Regina Vergilio
Aurora Trinidad Ramirez Pozo
Citations