A Note on Low Autocorrelation Binary Sequences

  • Iván Dotú
  • Pascal Van Hentenryck
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4204)


The Low Autocorrelation Binary Sequences problem (LABS) is problem 005 in the CSPLIB library, where it is stated that “these problems pose a significant challenge to local search methods”. This paper presents a straighforward tabu search that systematically finds the optimal solutions for all tested instances.


Local Search Tabu Search Optimal Sequence Local Search Method Merit Factor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Beenker, G., Claasen, T., Hermens, P.: Binary Sequences with a Maximally Flat Amplitude Spectrum. Philips Journal of Research 40, 289–304 (1985)zbMATHGoogle Scholar
  2. 2.
    Bernasconi, J.: Low Autocorrelation Binary Sequences: Statistical Mechanics and Configuration Space Analysis. Physique 48, 559 (1987)CrossRefGoogle Scholar
  3. 3.
    Brglez, F., Li, X., Stallman, M., Militzer, B.: Reliable Cost Prediction for Finding Optimal Solutions to LABS Problem: Evolutionary and Alternative Algorithms. In: Fifth International Workshop on Frontiers in Evolutionary Algorithms, Cary, NC, USA (2003)Google Scholar
  4. 4.
    Gent, I., Walsh, T.: CSPLIB: A Benchmark Library for Constraints,
  5. 5.
    Gent, I., Smith, B.: Symmetry Breaking During Search in Constraint Programming. Research Report, 99.02 (1999)Google Scholar
  6. 6.
    Golay, M.J.: Sieves for Low Autocorrelation Binary Sequences. IEEE Transactions on Information Theory 23, 41–43 (1977)CrossRefGoogle Scholar
  7. 7.
    Golay, M.J.: The Merit Factor of Long Low Autocorrelation Binary Sequences. IEEE Transactions on Information Theory 28, 543–549 (1982)CrossRefGoogle Scholar
  8. 8.
    de Groot, C., Wurtz, D., Hoffmann, K.: Low Autocorrelation Binary Sequences: Exact Enumeration and Optimization by Evolutionary Strategies. Optimization 23, 369–384 (1992)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Mertens, S.: Exhaustive Search for Low Autocorrelation Binary Sequences. J. Phys. A: Math. 29, 473–481 (1996)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Prestwich, S.D.: A Hybrid Local Search for Low Autocorrelation Binary Sequences. Technical Report, TR-00-01 (2000)Google Scholar
  11. 11.
    Prestwich, S.D.: A Hybrid Search Architecture Applied to Hard Random 3-SAT and Low-Autocorrelation Binary Sequences. In: Dechter, R. (ed.) CP 2000. LNCS, vol. 1894, pp. 337–352. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  12. 12.
    Prestwich, S.D.: Negative Effects of Modeling Techniques on Search Performance. Annals of Operations Research 118, 137–150 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Prestwich, S.D., Roli, A.: Symmetry breaking and local search spaces. In: Barták, R., Milano, M. (eds.) CPAIOR 2005. LNCS, vol. 3524, pp. 273–287. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  14. 14.
    Schroeder, M.R.: Number theory in Science and Communication, vol. 118, pp. 137–150. Springer, Berlin (2003)Google Scholar
  15. 15.
    Shapiro, I., Pettengil, G., Ash, M., Stone, M., Smith, W., Ingalls, R., Brockelman, R.: Fourth Test of General Relativity. Phys. rev. Lett. 20, 1265–1269 (1968)CrossRefGoogle Scholar
  16. 16.
    Wang, Q.: Optimization by Simulating Molecular Evolution. Biol. Cybern. 57, 95–101 (1987)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Iván Dotú
    • 1
  • Pascal Van Hentenryck
    • 2
  1. 1.Departamento De Ingeniería InformáticaUniversidad Autónoma de Madrid 
  2. 2.Brown UniversityProvidence

Personalised recommendations