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Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-540-85958-1_57
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dc.titleEngineering stochastic local search for the low autocorrelation binary sequence problem
dc.contributor.authorHalim, S.
dc.contributor.authorYap, R.H.C.
dc.contributor.authorHalim, F.
dc.date.accessioned2013-07-04T08:10:26Z
dc.date.available2013-07-04T08:10:26Z
dc.date.issued2008
dc.identifier.citationHalim, S., Yap, R.H.C., Halim, F. (2008). Engineering stochastic local search for the low autocorrelation binary sequence problem. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5202 LNCS : 640-645. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-540-85958-1_57
dc.identifier.isbn3540859578
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/40704
dc.description.abstractThis paper engineers a new state-of-the-art Stochastic Local Search (SLS) for the Low Autocorrelation Binary Sequence (LABS) problem. The new SLS solver is obtained with white-box visualization to get insights on how an SLS can be effective for LABS; implementation improvements; and black-box parameter tuning. © 2008 Springer-Verlag Berlin Heidelberg.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-540-85958-1_57
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1007/978-3-540-85958-1_57
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume5202 LNCS
dc.description.page640-645
dc.identifier.isiutNOT_IN_WOS
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