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dc.contributor.authorBrown, William Joshua
dc.date.accessioned2014-03-03T23:12:16Z
dc.date.available2014-03-03T23:12:16Z
dc.date.issued2005-05
dc.identifier.otherbrown_william_j_200505_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/brown_william_j_200505_ms
dc.identifier.urihttp://hdl.handle.net/10724/22310
dc.description.abstractA snake-in-the box code, first described in [Kautzl, ope1958]n p, atih s ian n aa chordahypercube. Finding bounds for the longest snake aptr oveeacn h tdio mbeens ai on difhafiscult problem in mathematics and computer science. Evolhautveiona sucryc eteedcehnid iqun es tightening the bounds of longest snakes in several di [mCeansseilonsla, [2005Pott]e.r , 1994]This thesis utilizes an Iterated Localh Saedaarptch ivehe urmeimsortiyc on witthe snake-in-the-box problem. The results match the best publishensd tarnceseuls tup s ftoro diprmoblenseimon i 8. The lack of implicit parallelism segrenteg aftreoms tprhiesvi eousxpe rheimuristics applied to this problem. As a result, this thesis provideesm si nsin igwhthi cih nteo volthosutieona prrobly methods domina te.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectsnake-in-the-box
dc.subjectsnake
dc.subjectcoil
dc.subjectGray code
dc.subjecterror correcting code
dc.subjectcircuit code
dc.subjecthypercube
dc.subjectiterated local search
dc.subjectILS
dc.subjectadaptive memory
dc.subjectAMP
dc.subjectheuristic
dc.subjectmetaheuristic
dc.subjectgraph theory
dc.titleAn iterated local search with adaptive memory applied to the snake in the box problem
dc.typeThesis
dc.description.degreeMS
dc.description.departmentComputer Science
dc.description.majorComputer Science
dc.description.advisorWalter D. Potter
dc.description.committeeWalter D. Potter
dc.description.committeeKhaled Rasheed
dc.description.committeeHamid R. Arabnia


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