Abstract
Hyper-heuristics are methodologies that choose from a set of heuristics and decide which one to apply given some properties of the current instance. When solving a constraint satisfaction problem, the order in which the variables are selected to be instantiated has implications in the complexity of the search. In this paper we propose a logistic regression model to generate hyper-heuristics for variable ordering within constraint satisfaction problems. The first step in our approach requires to generate a training set that maps any given instance, expressed in terms of some of their features, to one suitable variable ordering heuristic. This set is used later to train the system and generate a hyper-heuristic that decides which heuristic to apply given the current features of the instances at hand at different steps of the search. The results suggest that hyper-heuristics generated through this methodology allow us to exploit the strengths of the heuristics to minimize the cost of the search.
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Bilgin, B., Özcan, E., Korkmaz, E.E.: An experimental study on hyper-heuristics and exam timetabling. In: Burke, E.K., Rudová, H. (eds.) PATAT 2007. LNCS, vol. 3867, pp. 394–412. Springer, Heidelberg (2007)
Bitner, J.R., Reingold, E.M.: Backtrack programming techniques. Communications of the ACM 18(11), 651–656 (1975)
Bittle, S.A., Fox, M.S.: Learning and using hyper-heuristics for variable and value ordering in constraint satisfaction problems. In: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, pp. 2209–2212. ACM (2009)
Boussemart, F., Hemery, F., Lecoutre, C., Sais, L.: Boosting systematic search by weighting constraints. In: European Conference on Artificial Intelligence (ECAI 2004), pp. 146–150 (2004)
Burke, E., Hart, E., Kendall, G., Newall, J., Ross, P., Shulenburg, S.: Hyper-heuristics: an emerging direction in modern research technology. In: Handbook of Metaheuristics, pp. 457–474. Kluwer Academic Publishers (2003)
Burke, E.K., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., Woodward, J.R.: A classification of hyper-heuristic approaches. In: Gendreau, M., Potvin, J.Y. (eds.) Handbook of Metaheuristics. International Series in Operations Research and Management Science, vol. 146, pp. 449–468. Springer (2010)
Cowling, P., Kendall, G., Soubeiga, E.: Hyperheuristics: A robust optimisation method applied to nurse scheduling. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 851–860. Springer, Heidelberg (2002)
Fisher, H., Thompson, G.L.: Probabilistic learning combinations of local job-shop scheduling rules. In: Factory Scheduling Conference. Carnegie Institute of Technology (1961)
Freuder, E.C., Mackworth, A.K.: Constraint-Based Reasoning. MIT/Elsevier (1994)
Freuder, E.C.: Synthesizing constraint expressions. Communications of the ACM 21(11), 958–966 (1978)
Gaschnig, J.G.: A general backtrack algorithm that eliminates most redundant tests. In: Proceedings of the 5th International Joint Conference on Artificial Intelligence, vol. 1, p. 457. Morgan Kaufmann Publishers (1977)
Gent, I., MacIntyre, E., Prosser, P., Smith, B., Wals, 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)
Haralick, R.M., Elliott, G.L.: Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence 14, 263–313 (1980)
Kumar, V.: Algorithms for constraint satisfaction: a survey. AI Magazine 13(1), 32–44 (1992)
Lecoutre, C., Boussemart, F., Hemery, F.: Backjump-based techniques versus conflict-directed heuristics. In: Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2004, pp. 549–557. IEEE Computer Society, Washington, DC (2004)
Minton, S., Phillips, A., Laird, P.: Solving large-scale CSP and scheduling problems using a heuristic repair method. In: Proceedings of the 8th AAAI Conference, pp. 17–24 (1990)
Montanari, U.: Networks of constraints: fundamentals properties and applications to picture processing. Information Sciences 7, 95–132 (1974)
Ortiz-Bayliss, J., Terashima-Marín, H., Conant-Pablos, S.: Neural networks to guide the selection of heuristics within constraint satisfaction problems. In: Martínez-Trinidad, J., Carrasco-Ochoa, J., Ben-Youssef Brants, C., Hancock, E. (eds.) MCPR 2011. LNCS, vol. 6718, pp. 250–259. Springer, Heidelberg (2011)
Ortiz-Bayliss, J.C., Özcan, E., Parkes, A.J., Terashima-Marín, H.: Mapping the performance of heuristics for constraint satisfaction. In: Proceedings of the 2010 IEEE Congress on Evolutionary Computation (CEC 2010), pp. 1–8. IEEE Press (2010)
Ortiz-Bayliss, J.C., Terashima-Marín, H., Conant-Pablos, S.E.: Learning vector quantization for variable ordering in constraint satisfaction problems. Pattern Recogn. Lett. 34(4), 423–432 (2013)
Özcan, E., Bilgin, B., Korkmaz, E.E.: A comprehensive analysis of hyper-heuristics. Intelligent Data Analysis 12(1), 3–23 (2008)
Purdom, P.W.: Search rearrangement backtracking and polynomial average time. Artificial Intelligence 21, 117–133 (1983)
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)
Russell, S., Norvig, P.: Artificial Intelligence A Modern Approach. Prentice Hall (1995)
Smith, B.M.: Locating the phase transition in binary constraint satisfaction problems. Artificial Intelligence 81, 155–181 (1996)
Storer, R.H., Wu, S.D., Vaccari, R.: New search spaces for sequencing problems with application to job shop scheduling. Management Science 38(10), 1495–1509 (1992)
Terashima-Marín, H., Ortiz-Bayliss, J.C., Ross, P., Valenzuela-Rendón, M.: Hyper-heuristics for the dynamic variable ordering in constraint satisfaction problems. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation (GECCO 2008), pp. 571–578. ACM (2008)
Terashima-Marín, H., Ross, P., Valenzuela-Rendón, M.: Evolution of constraint satisfaction strategies in examination timetabling. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 1999), pp. 635–642. Morgan Kaufmann (1999)
Tsang, E., Kwan, A.: Mapping constraint satisfaction problems to algorithms and heuristics. Tech. Rep. CSM-198, Department of Computer Sciences, University of Essex (1993)
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Ortiz-Bayliss, J.C., Terashima-Marín, H., Conant-Pablos, S.E. (2013). A Supervised Learning Approach to Construct Hyper-heuristics for Constraint Satisfaction. In: Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Rodríguez, J.S., di Baja, G.S. (eds) Pattern Recognition. MCPR 2013. Lecture Notes in Computer Science, vol 7914. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38989-4_29
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