Automatic Derivation of Search Objectives for Test-Based Genetic Programming

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

@InProceedings{Krawiec:2015:EuroGP,
  author =       "Krzysztof Krawiec and Pawel Liskowski",
  title =        "Automatic Derivation of Search Objectives for
                 Test-Based Genetic Programming",
  booktitle =    "18th European Conference on Genetic Programming",
  year =         "2015",
  editor =       "Penousal Machado and Malcolm I. Heywood and 
                 James McDermott and Mauro Castelli and 
                 Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim",
  series =       "LNCS",
  volume =       "9025",
  publisher =    "Springer",
  pages =        "53--65",
  address =      "Copenhagen",
  month =        "8-10 " # apr,
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, Program
                 synthesis, Test-based problems, Multiobjective
                 evolutionary computation",
  isbn13 =       "978-3-319-16500-4",
  DOI =          "doi:10.1007/978-3-319-16501-1_5",
  abstract =     "In genetic programming (GP), programs are usually
                 evaluated by applying them to tests, and fitness
                 function indicates only how many of them have been
                 passed. We posit that scrutinising the outcomes of
                 programs interactions with individual tests may help
                 making program synthesis more effective. To this aim,
                 we propose DOC, a method that autonomously derives new
                 search objectives by clustering the outcomes of
                 interactions between programs in the population and the
                 tests. The derived objectives are subsequently used to
                 drive the selection process in a single or
                 multiobjective fashion. An extensive experimental
                 assessment on 15 discrete program synthesis tasks
                 representing two domains shows that DOC significantly
                 outperforms conventional GP and implicit fitness
                 sharing.",
  notes =        "Part of \cite{Machado:2015:GP} EuroGP'2015 held in
                 conjunction with EvoCOP2015, EvoMusArt2015 and
                 EvoApplications2015",
}

Genetic Programming entries for Krzysztof Krawiec Pawel Liskowski

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