Abstract
Search heuristics are of paramount importance for finding good solutions to optimization problems quickly. Manually designing problem specific search heuristics is a time consuming process and requires expert knowledge from the user. Thus there is great interest in developing autonomous search heuristics which work well for a wide variety of problems. Various autonomous search heuristics already exist, such as first fail, domwdeg and impact based search. However, such heuristics are often more focused on the variable selection, i.e., picking important variables to branch on to make the search tree smaller, rather than the value selection, i.e., ordering the subtrees so that the good subtrees are explored first. In this paper, we define a framework for learning value heuristics, by combining a scoring function, feature selection, and machine learning algorithm. We demonstrate that we can learn value heuristics that perform better than random value heuristics, and for some problem classes, the learned heuristics are comparable in performance to manually designed value heuristics. We also show that value heuristics using features beyond a simple score can be valuable.
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References
Haralick, R.M., Elliott, G.L.: Increasing tree search efficiency for constraint satisfaction problems. Artif. Intell. 14, 263–313 (1980)
Moskewicz, M.W., Madigan, C.F., Zhao, Y., Zhang, L., Malik, S.: Chaff: engineering an efficient SAT solver. In: Proceedings of the 38th Design Automation Conference, DAC 2001, pp. 530–535. ACM, Las Vegas, June 18–22, 2001
Boussemart, F., Hemery, F., Lecoutre, C., Sais, L.: Boosting systematic search by weighting constraints. In: de Mántaras, R.L., Saitta, L. (eds.) Proceedings of the 16th Eureopean Conference on Artificial Intelligence, ECAI 2004, Including Prestigious Applicants of Intelligent Systems, PAIS 2004, pp. 146–150. IOS Press, Valencia (2004)
Refalo, P.: Impact-based search strategies for constraint programming. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 557–571. Springer, Heidelberg (2004)
Zanarini, A., Pesant, G.: Solution counting algorithms for constraint-centered search heuristics. Constraints 14, 392–413 (2009)
Michel, L., Van Hentenryck, P.: Activity-based search for black-box constraint programming solvers. In: Beldiceanu, N., Jussien, N., Pinson, E. (eds.) CPAIOR 2012. LNCS, vol. 7298, pp. 228–243. Springer, Heidelberg (2012)
Pipatsrisawat, K., Darwiche, A.: A lightweight component caching scheme for satisfiability solvers. In: Marques-Silva, J., Sakallah, K.A. (eds.) SAT 2007. LNCS, vol. 4501, pp. 294–299. Springer, Heidelberg (2007)
Benichou, M., Gauthier, J., Girodet, P., Hentges, G., Ribiere, G., Vincent, O.: Experiments in mixed-integer programming. Mathematical Programming 1, 76–94 (1971)
Linderoth, J., Savelsbergh, M.: A computational study of search strategies for mixed integer programming. INFORMS Journal of Computing 11 (1999)
Kotthoff, L.: Algorithm selection for combinatorial search problems: A survey. CoRR abs/1210.7959 (2012)
Amemiya, T.: Advanced Econometrics. Harvard University Press (1985)
Wold, H.: Estimation of principal components and related models by iterative least squares. In: Multivariate Analysis, pp. 391–420. Academic Press (1966)
Chu, G., Stuckey, P.J.: Minimizing the maximum number of open stacks by customer search. In: Gent, I.P. (ed.) CP 2009. LNCS, vol. 5732, pp. 242–257. Springer, Heidelberg (2009)
Nethercote, N., Stuckey, P.J., Becket, R., Brand, S., Duck, G.J., Tack, G.R.: MiniZinc: towards a standard CP modelling language. In: Bessière, C. (ed.) CP 2007. LNCS, vol. 4741, pp. 529–543. Springer, Heidelberg (2007)
Garcia de la Banda, M., Stuckey, P., Chu, G.: Solving talent scheduling with dynamic programming. INFORMS Journal of Computing 23, 120–137 (2011)
Schutt, A., Feydy, T., Stuckey, P., Wallace, M.: Explaining the cumulative propagator. Constraints 16, 250–282 (2011)
Miller, H., Pierskalla, W., Rath, G.: Nurse scheduling using mathematical programming. Operations Research, 857–870 (1976)
Dincbas, M., Simonis, H., Van Hentenryck, P.: Solving the car-sequencing problem in constraint logic programming. In: ECAI, vol. 88, pp. 290–295 (1988)
Hartmann, S., Kolisch, R.: Experimental evaluation of state-of-the-art heuristics for resource constrained project scheduling. European Journal of Operational Research 127, 394–407 (2000)
Loth, M., Sebag, M., Hamadi, Y., Schoenauer, M.: Bandit-based search for constraint programming. In: Schulte, C. (ed.) CP 2013. LNCS, vol. 8124, pp. 464–480. Springer, Heidelberg (2013)
Savage, L.: The theory of statistical decision. Journal of the American Statistical Association 46 (1951)
Allouche, D., de Givry, S., Schiex, T.: Toulbar2, an open source exact cost function network solver. Technical report, INRIA (2010)
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Chu, G., Stuckey, P.J. (2015). Learning Value Heuristics for Constraint Programming. In: Michel, L. (eds) Integration of AI and OR Techniques in Constraint Programming. CPAIOR 2015. Lecture Notes in Computer Science(), vol 9075. Springer, Cham. https://doi.org/10.1007/978-3-319-18008-3_8
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DOI: https://doi.org/10.1007/978-3-319-18008-3_8
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