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International Conference on Intelligent Computing

ICIC 2010: Advanced Intelligent Computing Theories and Applications pp 252–257Cite as

GRASP for Low Autocorrelated Binary Sequences

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6215))

Abstract

The search for low autocorrelated binary sequences(LABS) is a combinatorial optimization problem, which is NP-hard. In this paper, we apply Greedy Randomized Adaptive Search Procedures (GRASP) to tackle the LABS problem. The algorithm is capable of systematically recovering best-known solutions reported by now. Furthermore, it can find out good autocorrelated binary sequences sequences in considerably less time as comparison with other heuristic methods.

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Author information

Authors and Affiliations

  1. School of Electronic Science and Engineering, Nanjing University, China

    Huchen Wang & Shaowei Wang

Authors
  1. Huchen Wang
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  2. Shaowei Wang
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Editor information

Editors and Affiliations

  1. Intelligent Computing Laboratory, Chinese Academy of Sciences, P.O. Box 1130, 230031, Hefei, Anhui, China

    De-Shuang Huang

  2. Department of Biomedical Informatics, Vanderbilt University Medical Center, 2,525 West End Avenue, Suite 600, 37203, Nashville, TN, USA

    Zhongming Zhao

  3. Electrical and Electronics Department, Polytechnic of Bari, Via Orabona 4, 70125, Bari, Italy

    Vitoantonio Bevilacqua

  4. Faculty of Engineering, District University Francisco José de Caldas, Cra. 7a,No. 40-53, Fifth Floor, Bogotá, Colombia

    Juan Carlos Figueroa

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© 2010 Springer-Verlag Berlin Heidelberg

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Cite this paper

Wang, H., Wang, S. (2010). GRASP for Low Autocorrelated Binary Sequences. In: Huang, DS., Zhao, Z., Bevilacqua, V., Figueroa, J.C. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Lecture Notes in Computer Science, vol 6215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14922-1_32

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  • DOI: https://doi.org/10.1007/978-3-642-14922-1_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14921-4

  • Online ISBN: 978-3-642-14922-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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