Evolution of team composition in multi-agent systems

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{DBLP:conf/gecco/RubiniHS09,
  author =       "Joshua Rubini and Robert B. Heckendorn and 
                 Terence Soule",
  title =        "Evolution of team composition in multi-agent systems",
  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 =        "1067--1074",
  address =      "Montreal",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "8-12 " # jul,
  organisation = "SigEvo",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-60558-325-9",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  DOI =          "doi:10.1145/1569901.1570045",
  abstract =     "Evolution of multi-agent teams has been shown to be an
                 effective method of solving complex problems involving
                 the exploration of an unknown problem space. These
                 autonomous and heterogeneous agents are able to go
                 places where humans are unable to go and perform tasks
                 that would be otherwise dangerous or impossible to
                 complete. This research tests the ability of the
                 Orthogonal Evolution of Teams (OET) algorithm to evolve
                 heterogeneous teams of agents which can change their
                 composition, i.e. the numbers of each type of agent on
                 a team. The results showed that OET could effectively
                 produce both the correct team composition and a team
                 for that composition that was competitive with teams
                 evolved with OET where the composition was fixed a
                 priori",
  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 Joshua Rubini Robert B Heckendorn Terence Soule

Citations