Abstract
Constraint programming is a powerful technology for the efficient solving of optimization and constraint satisfaction problems (CSPs). A main concern of this technology is that the efficient problem resolution usually relies on the employed solving strategy. Unfortunately, selecting the proper one is known to be complex as the behavior of strategies is commonly unpredictable. Recently, Autonomous Search appeared as a new technique to tackle this concern. The idea is to let the solver adapt its strategy during solving time in order to improve performance. This task is controlled by a choice function which decides, based on performance information, how the strategy must be updated. However, choice functions can be constructed in several manners variating the information used to take decisions. Such variations may certainly conduct to very different resolution processes. In this paper, we study the impact on the solving phase of 16 different carefully constructed choice functions. We employ as test bed a set of well-known benchmarks that collect general features present on most CSPs. Interesting experimental results are obtained in order to provide the best-performing choice functions for solving CSPs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
Crawford, B., Soto, R., Montecinos, M., Castro, C., Monfroy, E.: A framework for autonomous search in the Eclipse solver. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds.) IEA/AIE 2011, Part I. LNCS, vol. 6703, pp. 79–84. Springer, Heidelberg (2011)
Barták, R., Rudová, H.: Limited assignments: a new cutoff strategy for incomplete depth-firstsearch. In: Proceedings of the 20th ACM Symposium on Applied Computing (SAC), pp. 388–392 (2005)
Crawford, B., Castro, C., Monfroy, E., Soto, R., Palma, W., Paredes, F.: Dynamic selection of enumeration strategies for solving constraint satisfaction problems. Rom. J. Inf. Sci. Tech. 15(2), 106–128 (2012)
Crawford, B., Soto, R., Castro, C., Monfroy, E., Paredes, F.: An extensible autonomous search framework for constraint programming. Int. J. Phys. Sci. 6(14), 3369–3376 (2011)
Rossi, F.: Handbook of Constraint Programming. Elsevier, Amsterdam (2006)
Hamadi, Y., Monfroy, E., Saubion, F., Optimization, H.: What is autonomous search? In: van Hentenryck, P., Milano, M. (eds.) The Ten Years of CPAIOR. Springer, New York (2011)
Apt, K.R.: Principles of Constraint Programming. Cambridge Press, Cambridge (2003)
Maturana, J., Saubion, F.: A compass to guide genetic algorithms. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 256–265. Springer, Heidelberg (2008)
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)
Soto, R., Crawford, B., Misra, S., Palma, W., Monfroy, E., Castro, C., Paredes, F.: Choice functions for autonomous search in constraint programming: GA vs PSO. Tech. Gaz. 20(4), 621–629 (2013)
Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) NICSO 2010. SCI, vol. 284, pp. 65–74. Springer, Heidelberg (2010)
Yang, X.-S.: Bat algorithm for multi-objective optimisation. IJBIC 3(5), 267–274 (2011)
Yang, X.-S., He, X.: Bat algorithm: literature review and applications. IJBIC 5(3), 141–149 (2013)
Acknowledgments
Ricardo Soto is supported by Grant CONICYT/FONDECYT/REGULAR/1160455, Broderick Crawford is supported by Grant CONICYT/FONDECYT/REGULAR/1140897 and Rodrigo Olivares is supported by Postgraduate Grant Pontificia Universidad Católica de Valparaíso 2016.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Soto, R., Crawford, B., Olivares, R., Niklander, S., Olguín, E. (2016). The Impact of Using Different Choice Functions When Solving CSPs with Autonomous Search. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_77
Download citation
DOI: https://doi.org/10.1007/978-3-319-42007-3_77
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42006-6
Online ISBN: 978-3-319-42007-3
eBook Packages: Computer ScienceComputer Science (R0)