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.
Cite
CITATION STYLE
Halim, S., Yap, R. H. C., & Halim, F. (2008). Engineering stochastic local search for the low autocorrelation binary sequence problem. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5202 LNCS, pp. 640–645). https://doi.org/10.1007/978-3-540-85958-1_57
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