GP-rush: using genetic programming to evolve solvers for the Rush Hour puzzle

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@InProceedings{DBLP:conf/gecco/HauptmanESK09,
  author =       "Ami Hauptman and Achiya Elyasaf and Moshe Sipper and 
                 Assaf Karmon",
  title =        "{GP-rush:} using genetic programming to evolve solvers
                 for the {Rush Hour} puzzle",
  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 =        "955--962",
  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",
  URL =          "http://dl.acm.org/citation.cfm?id=1570032",
  DOI =          "doi:10.1145/1569901.1570032",
  abstract =     "We evolve heuristics to guide IDA* search for the 6x6
                 and 8x8 versions of the Rush Hour puzzle, a
                 PSPACE-Complete problem, for which no efficient solver
                 has yet been reported. No effective heuristic functions
                 are known for this domain, and--before applying any
                 evolutionary thinking--we first devise several novel
                 heuristic measures, which improve (non-evolutionary)
                 search for some instances, but hinder search
                 substantially for many other instances. We then turn to
                 genetic programming (GP) and find that evolution proves
                 immensely efficacious, managing to combine heuristics
                 of such highly variable utility into composites that
                 are nearly always beneficial, and far better than each
                 separate component. GP is thus able to beat both the
                 human player of the game and also the human designers
                 of heuristics.",
  notes =        "Also known as \cite{Hauptman2009}.

                 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 Ami Hauptman Achiya Elyasaf Moshe Sipper Assaf Karmon

Citations