• myGriffith
    • Staff portal
    • Contact Us⌄
      • Future student enquiries 1800 677 728
      • Current student enquiries 1800 154 055
      • International enquiries +61 7 3735 6425
      • General enquiries 07 3735 7111
      • Online enquiries
      • Staff phonebook
    View Item 
    •   Home
    • Griffith Research Online
    • Journal articles
    • View Item
    • Home
    • Griffith Research Online
    • Journal articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

  • All of Griffith Research Online
    • Communities & Collections
    • Authors
    • By Issue Date
    • Titles
  • This Collection
    • Authors
    • By Issue Date
    • Titles
  • Statistics

  • Most Popular Items
  • Statistics by Country
  • Most Popular Authors
  • Support

  • Contact us
  • FAQs
  • Admin login

  • Login
  • Constraint-based local search for Golomb rulers

    Thumbnail
    View/Open
    PolashPUB766.pdf (523.6Kb)
    Author(s)
    Polash, MM Alam
    Newton, MA Hakim
    Sattar, Abdul
    Griffith University Author(s)
    Sattar, Abdul
    Newton, MAHakim A.
    Polash, Md. Masbaul Alam MA.
    Year published
    2015
    Metadata
    Show full item record
    Abstract
    This paper presents a constraint-based local search algorithm to find an optimal Golomb ruler of a specified order. While the state-of-the-art search algorithms for Golomb rulers hybridise a range of sophisticated techniques, our algorithm relies on simple tabu meta-heuristics and constraint-driven variable selection heuristics. Given a reasonable time limit, our algorithm effectively finds 16-mark optimal rulers with success rate 60 % and 17-mark rulers with 6 % near-optimality.This paper presents a constraint-based local search algorithm to find an optimal Golomb ruler of a specified order. While the state-of-the-art search algorithms for Golomb rulers hybridise a range of sophisticated techniques, our algorithm relies on simple tabu meta-heuristics and constraint-driven variable selection heuristics. Given a reasonable time limit, our algorithm effectively finds 16-mark optimal rulers with success rate 60 % and 17-mark rulers with 6 % near-optimality.
    View less >
    Journal Title
    Lecture Notes in Computer Science
    Volume
    9075
    DOI
    https://doi.org/10.1007/978-3-319-18008-3_22
    Copyright Statement
    © 2015 Springer International Publishing AG. This is an electronic version of an article published in Lecture Notes In Computer Science (LNCS), Vol. 9075, pp 322-331, 2015. Lecture Notes In Computer Science (LNCS) is available online at: http://link.springer.com// with the open URL of your article.
    Subject
    Bioinformatics
    Publication URI
    http://hdl.handle.net/10072/125178
    Collection
    • Journal articles

    Footer

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E

    Tagline

    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander