Evolution of hyperheuristics for the biobjective graph coloring problem using multiobjective genetic programming

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

@InProceedings{DBLP:conf/gecco/TolayK09,
  author =       "Paresh Tolay and Rajeev Kumar",
  title =        "Evolution of hyperheuristics for the biobjective graph
                 coloring problem using multiobjective genetic
                 programming",
  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 =        "1939--1940",
  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, http://dblp.uni-trier.de",
  DOI =          "doi:10.1145/1569901.1570247",
  abstract =     "We consider a formulation of the biobjective soft
                 graph coloring problem so as to simultaneously minimize
                 the number of colors used as well as the number of
                 edges that connect vertices of the same color. We aim
                 to evolve hyperheuristics for this class of problem
                 using multiobjective genetic programming (MOGP). The
                 major advantage being that these hyperheuristics can
                 then be applied to any instance of this problem. We
                 test the hyperheuristics on benchmark graph coloring
                 problems, and in the absence of an actual Pareto-front,
                 we compare the solutions obtained with existing
                 heuristics. We then further improve the quality of
                 hyperheuristics evolved, and try to make them closer to
                 human-designed heuristics.",
  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 Paresh Tolay Rajeev Kumar

Citations