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What Makes a Problem GP-Hard? Analysis of a Tunably Difficult Problem in Genetic Programming
dc.contributor.authorDaida, Jason M.en_US
dc.contributor.authorBertram, Robert R.en_US
dc.contributor.authorStanhope, Stephen A.en_US
dc.contributor.authorKhoo, Jonathan C.en_US
dc.contributor.authorChaudhary, Shahbaz A.en_US
dc.contributor.authorChaudhri, Omer A.en_US
dc.contributor.authorPolito, John A. II.en_US
dc.date.accessioned2006-09-11T16:15:58Z
dc.date.available2006-09-11T16:15:58Z
dc.date.issued2001-06en_US
dc.identifier.citationDaida, Jason M.; Bertram, Robert R.; Stanhope, Stephen A.; Khoo, Jonathan C.; Chaudhary, Shahbaz A.; Chaudhri, Omer A.; Polito, John A. II; (2001). "What Makes a Problem GP-Hard? Analysis of a Tunably Difficult Problem in Genetic Programming." Genetic Programming and Evolvable Machines 2(2): 165-191. <http://hdl.handle.net/2027.42/45613>en_US
dc.identifier.issn1389-2576en_US
dc.identifier.issn1573-7632en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/45613
dc.description.abstractThis paper addresses the issue of what makes a problem genetic programming (GP)-hard by considering the binomial-3 problem. In the process, we discuss the efficacy of the metaphor of an adaptive fitness landscape to explain what is GP-hard. We indicate that, at least for this problem, the metaphor is misleading.en_US
dc.format.extent431054 bytes
dc.format.extent3115 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_US
dc.publisherKluwer Academic Publishers; Springer Science+Business Mediaen_US
dc.subject.otherComputer Scienceen_US
dc.subject.otherSoftware Engineering/Programming and Operating Systemsen_US
dc.subject.otherProgramming Languages, Compilers, Interpretersen_US
dc.subject.otherArtificial Intelligence (Incl. Robotics)en_US
dc.subject.otherProblem Difficultyen_US
dc.subject.otherTest Problemsen_US
dc.subject.otherFitness Landscapesen_US
dc.subject.otherGP Theoryen_US
dc.titleWhat Makes a Problem GP-Hard? Analysis of a Tunably Difficult Problem in Genetic Programmingen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelPhilosophyen_US
dc.subject.hlbsecondlevelComputer Scienceen_US
dc.subject.hlbtoplevelEngineeringen_US
dc.subject.hlbtoplevelHumanitiesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumArtificial Intelligence Laboratory and Space Physics Research Laboratory, The University of Michigan, 2455 Hayward Avenue, Ann Arbor, MI, 48109-2143, USAen_US
dc.contributor.affiliationumArtificial Intelligence Laboratory and Space Physics Research Laboratory, The University of Michigan, 2455 Hayward Avenue, Ann Arbor, MI, 48109-2143, USAen_US
dc.contributor.affiliationumArtificial Intelligence Laboratory and Space Physics Research Laboratory, The University of Michigan, 2455 Hayward Avenue, Ann Arbor, MI, 48109-2143, USAen_US
dc.contributor.affiliationumArtificial Intelligence Laboratory and Space Physics Research Laboratory, The University of Michigan, 2455 Hayward Avenue, Ann Arbor, MI, 48109-2143, USAen_US
dc.contributor.affiliationumArtificial Intelligence Laboratory and Space Physics Research Laboratory, The University of Michigan, 2455 Hayward Avenue, Ann Arbor, MI, 48109-2143, USAen_US
dc.contributor.affiliationumArtificial Intelligence Laboratory and Space Physics Research Laboratory, The University of Michigan, 2455 Hayward Avenue, Ann Arbor, MI, 48109-2143, USAen_US
dc.contributor.affiliationotherConsilient, Inc., 1815 4th Street, Suite B, Berkeley, CA, 94710, USAen_US
dc.contributor.affiliationumcampusAnn Arboren_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/45613/1/10710_2004_Article_335714.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1023/A:1011504414730en_US
dc.identifier.sourceGenetic Programming and Evolvable Machinesen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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