Automatic Generation of Heuristics for Constraint Satisfaction Problems

  • José Carlos Ortiz-BaylissEmail author
  • Jorge Humberto Moreno-Scott
  • Hugo Terashima-Marín
Part of the Studies in Computational Intelligence book series (SCI, volume 512)


The constraint satisfaction problem (CSP) is a generic problem with many applications in different areas of artificial intelligence and operational research. When solving a CSP, the order in which the variables are selected to be instantiated has a tremendous impact in the cost of finding a solution. In this paper we explore a novel type of heuristic that combines different features that describe the current state of the instance to decide which variable to instantiate next. A generational genetic algorithm is used to automatically tune the parameters used by these new heuristics. This paper contributes to the development of new heuristics that can be either very specialized to one class of instances, or general enough to deal with different classes of instances with an acceptable performance.


Constraint Satisfaction Heuristics Genetic Algorithms 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Achlioptas, D., Molloy, M.S.O., Kirousis, L.M., Stamatiou, Y.C., Kranakis, E., Krizanc, D.: Random constraint satisfaction: A more accurate picture. Constraints 6(4), 329–344 (2001)MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Bain, S., Thornton, J., Sattar, A.: Evolving algorithms for constraint satisfaction. In: Congress on Evolutionary Computation 2004 (CEC 2004), vol. 1, pp. 265–272 (2004a)Google Scholar
  3. 3.
    Bain, S., Thornton, J., Sattar, A.: Methods of automatic algorithm generation. In: Zhang, C., Guesgen, H.W., Yeap, W.K. (eds.) PRICAI 2004. LNCS (LNAI), vol. 3157, pp. 144–153. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Berlier, J., McCollum, J.: A constraint satisfaction algorithm for microcontroller selection and pin assignment. In: Proceedings of the IEEE SoutheastCon 2010 (SoutheastCon), pp. 348–351 (2010)Google Scholar
  5. 5.
    Bessière, C., Régin, J.C.: Mac and combined heuristics: Two reasons to forsake FC (and CBJ) on hard problems. In: Freuder, E.C. (ed.) CP 1996. LNCS, vol. 1118, Springer, Heidelberg (1996)Google Scholar
  6. 6.
    Burke, E.K., Hyde, M.R., Kendall, G., Ochoa, G., Ozcan, E., Woodward, J.R.: Exploring hyper-heuristic methodologies with genetic programming. In: Mumford, C.L., Jain, L.C. (eds.) Computational Intelligence. ISRL, vol. 1, pp. 177–201. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Burke, E.K., Hyde, M.R., Kendall, G., Woodward, J.: Automatic heuristic generation with genetic programming: evolving a jack-of-all-trades or a master of one. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, GECCO 2007, pp. 1559–1565. ACM, New York (2007)CrossRefGoogle Scholar
  8. 8.
    Crawford, B., Soto, R., Castro, C., Monfroy, E.: A hyperheuristic approach for dynamic enumeration strategy selection in constraint satisfaction. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds.) IWINAC 2011, Part II. LNCS, vol. 6687, pp. 295–304. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Dunkin, N., Allen, S.: Frequency assignment problems: Representations and solutions. Tech. Rep. CSD-TR-97-14, University of London (1997)Google Scholar
  10. 10.
    Epstein, S.L., Freuder, E.C., Wallace, R.J., Morozov, A., Samuels, B.: The adaptive constraint engine. In: Van Hentenryck, P. (ed.) CP 2002. LNCS, vol. 2470, pp. 525–542. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  11. 11.
    Gaschnig, J.: Experimental case studies of backtrack vs. waltz-type vs. new algorithms for satisficing assignment problems. In: Proceedings of the Canadian Artificial Intelligence Conference, pp. 268–277 (1978)Google Scholar
  12. 12.
    Gent, I., MacIntyre, E., Prosser, P., Smith, B., Walsh, T.: An empirical study of dynamic variable ordering heuristics for the constraint satisfaction problem. In: Freuder, E.C. (ed.) CP 1996. LNCS, vol. 1118, pp. 179–193. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  13. 13.
    Holland, J.: Adaptation in Natural and Artificial Systems. The University of Michigan Press (1975)Google Scholar
  14. 14.
    MacIntyre, E., Prosser, P., Smith, B.M., Walsh, T.: Random constraint satisfaction: Theory meets practice. In: Maher, M.J., Puget, J.-F. (eds.) CP 1998. LNCS, vol. 1520, pp. 325–339. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  15. 15.
    Mackworth, A.K.: Consistency in networks of relations. Artificial Intelligence 8(1), 99–118 (1977)MathSciNetzbMATHCrossRefGoogle Scholar
  16. 16.
    Minton, S.: An analytic learning system for specializing heuristics. In: Proceedings of the 13th International Joint Conference on Artificial Intelligence (IJCAI 1993), pp. 922–929. Morgan Kaufmann (1993)Google Scholar
  17. 17.
    Minton, S., Johnston, M.D., Phillips, A., Laird, P.: Minimizing conflicts: A heuristic repair method for CSP and scheduling problems. Artificial Intelligence 58, 161–205 (1992)MathSciNetzbMATHCrossRefGoogle Scholar
  18. 18.
    O’Mahony, E., Hebrard, E., Holland, A., Nugent, C., O’Sullivan, B.: Using case-based reasoning in an algorithm portfolio for constraint solving. In: Proceedings of the 19th Irish Conference on Artificial Intelligence and Cognitive Science (2008)Google Scholar
  19. 19.
    Ortiz-Bayliss, J.C., Terashima-Marín, H., Conant-Pablos, S.E.: Learning vector quantization for variable ordering in constraint satisfaction problems. Pattern Recognition Letters 34(4), 423–432 (2013)CrossRefGoogle Scholar
  20. 20.
    Petrovic, S., Qu, R.: Case-based reasoning as a heuristic selector in a hyper-heuristic for course timetabling problems. In: Proceedings of the 6th International Conference on Knowledge-Based Intelligent Information Engineering Systems and Applied Technologies (KES 2002), vol. 82, pp. 336–340 (2002)Google Scholar
  21. 21.
    Rossi, F., Petrie, C., Dhar, V.: On the equivalence of constraint satisfaction problems. In: Proceedings of the 9th European Conference on Artificial Intelligence, pp. 550–556 (1990)Google Scholar
  22. 22.
    Schwartz, S., Wah, B.: Automated parameter tuning in stereo vision under time constraints. In: Proceedings., Fourth International Conference on Tools with Artificial Intelligence, TAI 1992, pp. 162–169 (1992)Google Scholar
  23. 23.
    Soto, R., Crawford, B., Monfroy, E., Bustos, V.: Using autonomous search for generating good enumeration strategy blends in constraint programming. In: Murgante, B., Gervasi, O., Misra, S., Nedjah, N., Rocha, A.M.A.C., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2012, Part III. LNCS, vol. 7335, pp. 607–617. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  24. 24.
    Wallace, R.J.: Analysis of heuristic synergies. In: Hnich, B., Carlsson, M., Fages, F., Rossi, F. (eds.) CSCLP 2005. LNCS (LNAI), vol. 3978, pp. 73–87. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  25. 25.
    Williams, C.P., Hogg, T.: Using deep structure to locate hard problems. In: Proceedings of AAAI 1992, pp. 472–477 (1992)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • José Carlos Ortiz-Bayliss
    • 1
    Email author
  • Jorge Humberto Moreno-Scott
    • 2
  • Hugo Terashima-Marín
    • 2
  1. 1.Automated Scheduling, Optimisation and Planning (ASAP) School of Computer ScienceUniversity of NottinghamNottinghamUK
  2. 2.Tecnológico de MonterreyMonterreyMexico

Personalised recommendations