An adaptive strategy for improving the performance of genetic programming-based approaches to evolutionary testing

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Jose Carlos {Bregieiro Ribeiro} and 
                 Mario {Zenha Rela} and Francisco {Fernandez de Vega}",
  title =        "An adaptive strategy for improving the performance of
                 genetic programming-based approaches to evolutionary
  booktitle =    "GECCO '09: Proceedings of the 11th Annual conference
                 on Genetic and evolutionary computation",
  year =         "2009",
  editor =       "Guenther Raidl and Franz Rothlauf and 
                 Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and 
                 Mauro Birattari and Clare Bates Congdon and 
                 Martin Middendorf and Christian Blum and Carlos Cotta and 
                 Peter Bosman and Joern Grahl and Joshua Knowles and 
                 David Corne and Hans-Georg Beyer and Ken Stanley and 
                 Julian F. Miller and Jano {van Hemert} and 
                 Tom Lenaerts and Marc Ebner and Jaume Bacardit and 
                 Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and 
                 Thomas Jansen and Riccardo Poli and Enrique Alba",
  pages =        "1949--1950",
  address =      "Montreal",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "8-12 " # jul,
  organisation = "SigEvo",
  keywords =     "genetic algorithms, genetic programming, Poster",
  isbn13 =       "978-1-60558-325-9",
  bibsource =    "DBLP,",
  DOI =          "doi:10.1145/1569901.1570253",
  abstract =     "This paper proposes an adaptive strategy for enhancing
                 Genetic Programming-based approaches to automatic test
                 case generation. The main contribution of this study is
                 that of proposing an adaptive Evolutionary Testing
                 methodology for promoting the introduction of relevant
                 instructions into the generated test cases by means of
                 mutation; the instructions from which the algorithm can
                 choose are ranked, with their rankings being updated
                 every generation in accordance to the feedback obtained
                 from the individuals evaluated in the preceding
                 generation. The experimental studies developed show
                 that the adaptive strategy proposed improves the
                 algorithm's efficiency considerably, while introducing
                 a negligible computational overhead.",
  notes =        "GECCO-2009 A joint meeting of the eighteenth
                 international conference on genetic algorithms
                 (ICGA-2009) and the fourteenth annual genetic
                 programming conference (GP-2009).

                 ACM Order Number 910092.",

Genetic Programming entries for Jose Carlos Bregieiro Ribeiro Mario Alberto Zenha-Rela Francisco Fernandez de Vega