GP-rush: using genetic programming to evolve solvers for the Rush Hour puzzle
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @InProceedings{DBLP:conf/gecco/HauptmanESK09,
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author = "Ami Hauptman and Achiya Elyasaf and Moshe Sipper and
Assaf Karmon",
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title = "{GP-rush:} using genetic programming to evolve solvers
for the {Rush Hour} puzzle",
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booktitle = "GECCO '09: Proceedings of the 11th Annual conference
on Genetic and evolutionary computation",
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year = "2009",
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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",
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pages = "955--962",
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address = "Montreal",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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month = "8-12 " # jul,
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organisation = "SigEvo",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-60558-325-9",
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bibsource = "DBLP, http://dblp.uni-trier.de",
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URL = "http://dl.acm.org/citation.cfm?id=1570032",
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DOI = "doi:10.1145/1569901.1570032",
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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.",
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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