{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/low-autocorrelation-binary-sequences-on","title":"Low-Autocorrelation Binary Sequences: On Improved Merit Factors and Runtime Predictions to Achieve Them","arxiv_id":"1406.5301","date":"2014-06-20","proceeding":null,"authors":["Borko Bošković","Franc Brglez","Janez Brest"],"abstract":"The search for binary sequences with a high figure of merit, known as the low\nautocorrelation binary sequence ($labs$}) problem, represents a formidable\ncomputational challenge. To mitigate the computational constraints of the\nproblem, we consider solvers that accept odd values of sequence length $L$ and\nreturn solutions for skew-symmetric binary sequences only -- with the\nconsequence that not all best solutions under this constraint will be optimal\nfor each $L$. In order to improve both, the search for best merit factor $and$\nthe asymptotic runtime performance, we instrumented three stochastic solvers,\nthe first two are state-of-the-art solvers that rely on variants of memetic and\ntabu search ($lssMAts$ and $lssRRts$), the third solver ($lssOrel$) organizes\nthe search as a sequence of independent contiguous self-avoiding walk segments.\nBy adapting a rigorous statistical methodology to performance testing of all\nthree combinatorial solvers, experiments show that the solver with the best\nasymptotic average-case performance, $lssOrel\\_8 = 0.000032*1.1504^L$, has the\nbest chance of finding solutions that improve, as $L$ increases, figures of\nmerit reported to date. The same methodology can be applied to engineering new\n$labs$ solvers that may return merit factors even closer to the conjectured\nasymptotic value of 12.3248.","url_abs":"http://arxiv.org/abs/1406.5301v6","url_pdf":"http://arxiv.org/pdf/1406.5301v6.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"low-autocorrelation-binary-sequences-on","repo_url":"https://github.com/borkob/git_labs","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}