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dc.contributor.authorda Silva, Rodrigo Ferreira
dc.contributor.authorHvattum, Lars Magnus
dc.contributor.authorGlover, Fred
dc.date.accessioned2022-11-15T10:04:47Z
dc.date.available2022-11-15T10:04:47Z
dc.date.created2020-09-02T14:37:05Z
dc.date.issued2020
dc.identifier.citationThe MENDEL Soft Computing Journal : International Conference on Soft Computing MENDEL. 2020, 26 (1), 23-29.en_US
dc.identifier.issn1803-3814
dc.identifier.urihttps://hdl.handle.net/11250/3031861
dc.description.abstractThe optimum satisfiability problem involves determining values for Boolean vari- ables to satisfy a Boolean expression, while maximizing the sum of coefficients associated with the variables chosen to be true. Existing literature has identified a tabu search heuristic as the best method to deal with hard instances of the prob- lem. This paper combines the tabu search with a simple evolutionary heuristic based on the idea of tunneling between local optima. When combining a set of solutions, variables with common values in all solutions are identified and fixed. The remaining free variables in the problem may be decomposed into several in- dependent subproblems, so that parts of the solutions combined can be extracted and combined in an improved solution. This solution can be further improved by applying the tabu search in an improvement stage. The value of the new heuristic is demonstrated in extensive computational experiments on both existing and new test instances. Keywords: zero-one integer programming, boolean optimization, metaheuristic, tabu search, adaptive memory programming, recombination operator. Keywords: zero-one integer programming, boolean optimization, metaheuristic, tabu search, adaptive memory programming, recombination operatoren_US
dc.language.isoengen_US
dc.rightsNavngivelse-Ikkekommersiell-DelPåSammeVilkår 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/deed.no*
dc.titleCombining solutions of the optimum satisfiability problem using evolutionary tunnelingen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber23-29en_US
dc.source.volume26en_US
dc.source.journalThe MENDEL Soft Computing Journal : International Conference on Soft Computing MENDELen_US
dc.source.issue1en_US
dc.identifier.doi10.13164/mendel.2020.1.023
dc.identifier.cristin1826818
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Navngivelse-Ikkekommersiell-DelPåSammeVilkår 4.0 Internasjonal
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