IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v577y2021ics0378437121003629.html
   My bibliography  Save this article

Complex networks analysis of the energy landscape of the low autocorrelation binary sequences problem

Author

Listed:
  • Tomassini, Marco

Abstract

We provide an up-to-date view of the structure of the energy landscape of the low autocorrelation binary sequences problem, a typical representative of the NP-hard class. To study the landscape features of interest we use the local optima network methodology through exhaustive extraction of the optima graphs for problem sizes up to 24. Several metrics are used to characterize the networks: number and type of optima, optima basins structure, degree and strength distributions, shortest paths to the global optima, and random walk-based centrality of optima. Taken together, these metrics provide a quantitative and coherent explanation for the difficulty of the low autocorrelation binary sequences problem and provide information that could be exploited by optimization heuristics for this problem, as well as for a number of other problems having a similar configuration space structure.

Suggested Citation

  • Tomassini, Marco, 2021. "Complex networks analysis of the energy landscape of the low autocorrelation binary sequences problem," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 577(C).
    • Handle: RePEc:eee:phsmap:v:577:y:2021:i:c:s0378437121003629
    DOI: 10.1016/j.physa.2021.126089
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437121003629
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2021.126089?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fernando F. Feirreira & José F. Fontanari & Peter F. Stadler, 2000. "Landscape Statistics of the Low Autocorrelated Binary String Problem," Working Papers 00-07-033, Santa Fe Institute.
    2. Sebastian Herrmann & Gabriela Ochoa & Franz Rothlauf, 2018. "PageRank centrality for performance prediction: the impact of the local optima network model," Journal of Heuristics, Springer, vol. 24(3), pages 243-264, June.
    3. Daolio, Fabio & Tomassini, Marco & Vérel, Sébastien & Ochoa, Gabriela, 2011. "Communities of minima in local optima networks of combinatorial spaces," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(9), pages 1684-1694.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Christoph Flamm & Ivo L. Hofacker & Peter F. Stadler & Michael T. Wolfinger, 2001. "Barrier Trees of Degenerate Landscapes," Working Papers 01-09-053, Santa Fe Institute.
    2. Sun, Yixiang & Du, Haifeng & Gong, Maoguo & Ma, Lijia & Wang, Shanfeng, 2014. "Fast computing global structural balance in signed networks based on memetic algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 261-272.
    3. Oliver Bastert & Dan Rockmore & Peter F. Stadler & Gottfried Tinhofer, 2001. "Landscapes on Spaces of Trees," Working Papers 01-01-006, Santa Fe Institute.
    4. Sebastian Herrmann & Gabriela Ochoa & Franz Rothlauf, 2016. "Communities of Local Optima as Funnels in Fitness Landscapes," Working Papers 1609, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    5. Tamás Vinkó & Kitti Gelle, 2017. "Basin Hopping Networks of continuous global optimization problems," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(4), pages 985-1006, December.
    6. Gabriela Ochoa & Nadarajen Veerapen, 2018. "Mapping the global structure of TSP fitness landscapes," Journal of Heuristics, Springer, vol. 24(3), pages 265-294, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:577:y:2021:i:c:s0378437121003629. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.