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dc.contributor.authorIshtaiwi, Abdelraoufen_US
dc.contributor.authorThornton, Johnen_US
dc.contributor.authorAnbulagan, Noneen_US
dc.contributor.authorSattar, Abdulen_US
dc.contributor.authorPham, Nghiaen_US
dc.contributor.editorFrederic Benhamouen_US
dc.date.accessioned2017-05-03T12:54:29Z
dc.date.available2017-05-03T12:54:29Z
dc.date.issued2006en_US
dc.identifier.refurihttp://www.sciences.univ-nantes.fr/cp06/en_US
dc.identifier.urihttp://hdl.handle.net/10072/13102
dc.description.abstractIn recent years, dynamic local search (DLS) clause weighting algorithms have emerged as the local search state-of-the-art for solving propositional satisfiability problems. However, most DLS algorithms require the tuning of domain dependent parameters before their performance becomes competitive. If manual parameter tuning is impractical then various mechanisms have been developed that can automatically adjust a parameter value during the search. To date, the most effective adaptive clause weighting algorithm is RSAPS. However, RSAPS is unable to convincingly outperform the best non-weighting adaptive algorithm AdaptNovelty+, even though manually tuned clause weighting algorithms can routinely outperform the Novelty+ heuristic on which AdaptNovelty+ is based. In this study we introduce R+DDFW+, an enhanced version of the DDFWclause weighting algorithmdeveloped in 2005, that not only adapts the total amount of weight according to the degree of stagnation in the search, but also incorporates the latest resolution-based preprocessing approach used by the winner of the 2005 SAT competition (R+ AdaptNovelty+). In an empirical study we show R+DDFW+ improves on DDFW and outperforms the other leading adaptive (R+Adapt-Novelty+, R+RSAPS) and non-adaptive (R+G2WSAT) local search solvers over a range of random and structured benchmark problems.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.format.extent222848 bytes
dc.format.extent44136 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherSpringer-Verlagen_US
dc.publisher.placeBerlinen_US
dc.publisher.urihttp://www.springer.com/east/home/generic/search/results?SGWID=5-40109-22-173681505-0en_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofconferencename12th International Conference on the Principles and Practice of Constraint Programming (CP 2006)en_US
dc.relation.ispartofconferencetitlePrinciples and Practice of Constraint Programming - CP 2006en_US
dc.relation.ispartofdatefrom2006-09-24en_US
dc.relation.ispartofdateto2006-09-29en_US
dc.relation.ispartoflocationNantes, Franceen_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchcode280213en_US
dc.titleAdaptive Clause Weight Redistributionen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.rights.copyrightCopyright 2006 Springer. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.comen_US
gro.date.issued2015-06-01T23:37:04Z
gro.hasfulltextFull Text


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