Engineering stochastic local search for the low autocorrelation binary sequence problem

Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-540-85958-1_57

Title:	Engineering stochastic local search for the low autocorrelation binary sequence problem
Authors:	Halim, S. Yap, R.H.C. Halim, F.
Issue Date:	2008
Citation:	Halim, 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
Abstract:	This 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.
Source Title:	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI:	http://scholarbank.nus.edu.sg/handle/10635/40704
ISBN:	3540859578
ISSN:	03029743
DOI:	10.1007/978-3-540-85958-1_57
Appears in Collections:	Staff Publications

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