A Grammar-guided Genetic Programming Framework Configured for Data Mining and Software Testing

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{journals/ijseke/VergilioP06,
  title =        "A Grammar-guided Genetic Programming Framework
                 Configured for Data Mining and Software Testing",
  author =       "Silvia Regina Vergilio and 
                 Aurora Trinidad Ramirez Pozo",
  journal =      "International Journal of Software Engineering and
                 Knowledge Engineering",
  year =         "2006",
  number =       "2",
  volume =       "16",
  pages =        "245--268",
  bibdate =      "2006-05-22",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/ijseke/ijseke16.html#VergilioP06",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 computation, data mining, software testing, grammars",
  DOI =          "doi:10.1142/S0218194006002781",
  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