Abstract
The idea behind hyper-heuristics is to discover some combination of straightforward heuristics to solve a wide range of problems. To be worthwhile, such combination should outperform the single heuristics. This paper presents a GA-based method that produces general hyper-heuristics for the dynamic variable ordering within Constraint Satisfaction Problems. The GA uses a variable-length representation, which evolves combinations of condition-action rules producing hyper-heuristics after going through a learning process which includes training and testing phases. Such hyper-heuristics, when tested with a large set of benchmark problems, produce encouraging results for most of the cases. There are instances of CSP that are harder to be solved than others, this due to the constraint and the conflict density [4]. The testebed is composed of hard problems randomly generated by an algorithm proposed by Prosser [18].
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References
Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic programming: An Introduction. Morgan Kaufmann Publishers, Inc., London (1998)
Brelaz, D.: New methods to colour the vertices of a graph. Comunications of the ACM 22 (1979)
Burke, E., Hart, E., Kendall, G., Newall, J., Ross, P., Schulenburg, S.: Hyper-heuristics: An emerging direction in modern research technolology. In: Handbook of Metaheuristics, pp. 457–474. Kluwer Academic Publishers, Dordrecht (2003)
Cheeseman, P., Kanefsky, B., Taylor, W.M.: Where the real hard problems are. In: Proceedings of IJCAI 1991, pp. 331–337 (1991)
Craenen, B.G.W., Eiben, A.E., van Hemert, J.I.: Comparing evolutionary algorithms on binary constraint satisfaction problems. IEEE Transactions on Evolutionary Computation 7(5), 424–444 (2003)
Dechter, R., Meiri, I.: Experimental evaluation of preprocessing algorithms for constraint satisfaction problems. Artificial Intelligence 38(2), 211–242 (1994)
Fogel, D.B., Owens, L.A., Walsh, M.: Artificial Intelligence through Simulated Evolution. Wiley, New York (1966)
Garey, M., Johnson, D.: Computers and Intractability. W.H. Freeman and Company, New York (1979)
Gent, I.P., MacIntyre, E., Prosser, P., Smith, B.M., 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)
Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading (1989)
Goldberg, D., Korb, B., Deb, K.: Messy genetic algorithms: Motivation, analysis and first results. Complex Systems, 93–130 (1989)
Golden, B.L.: Approaches to the cutting stock problem. AIIE Transactions 8, 256–274 (1976)
Haralick, R.M., Elliott, G.L.: Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence 14, 263–313 (1980)
Holland, J.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)
Marchiori, E., Steenbeek, A.: A genetic local search algorithm for random binary constraint satisfaction problems. In: Proceedings of the 2000 ACM symposium on Applied computing, Como, Italy, vol. 1, pp. 458–462 (2000)
Minton, S., Johnston, M.D., Phillips, A., Laird, P.: Minimizing conflicts: A heuristic repair method for csp and scheduling problems. Artificial Intellgence 58, 161–205 (1992)
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)
Prosser, P.: Binary constraint satisfaction problems: Some are harder than others. In: Proceedings of the European Conference in Artificial Intelligence, Amsterdam, Holland, pp. 95–99 (1994)
Rechenberg, I.: Evolutionstrategie: Optimierung technischer systeme nach prinzipien dier biolischen evolution. Frommann-Holzboog, Stuttgart (1973)
Ross, P., Blázquez, J.M., Schulenburg, S., Hart, E.: Learning a procedure that can solve hard bin-packing problems: A new ga-based approach to hyper-heuristics. In: Proceedings of GECCO 2003, pp. 1295–1306 (2003)
Russell, S., Norvig, P.: Artificial Intelligence A Modern Approach. Prentice Hall, Englewood Cliffs (1995)
Schwefel, H.P.: Numerical Optimization of Computer Models. Wiley, Chichester (1981)
Terashima-Marín, H., Calleja-Manzanedo, R., Valenzuela-Rendón, M.: Genetic Algorithms for Dynamic Variable Ordering in Constraint Satisfaction Problems. Advances in Artificial Intelligence Theory 16, 35–44 (2005)
Terashima-Marín, H., Farías-Zárate, C.J., Ross, P., Valenzuela-Rendón, M.: A GA-Based Method to Produce Generalized Hyper-heuristics for the 2D-Regular Cutting Stock Problem. In: Proceedings of the 8th annual conference on Genetic and evolutionary computation, Seattle, Washington, USA, pp. 591–598 (2006)
Wilson, R.A., Keil, F.C.: The MIT Encyclopedia of the Cognitive Science. MIT Press, Cambridge (1999)
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Terashima-Marín, H., Ortiz-Bayliss, J.C., Ross, P., Valenzuela-Rendón, M. (2008). Using Hyper-heuristics for the Dynamic Variable Ordering in Binary Constraint Satisfaction Problems. In: Gelbukh, A., Morales, E.F. (eds) MICAI 2008: Advances in Artificial Intelligence. MICAI 2008. Lecture Notes in Computer Science(), vol 5317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88636-5_39
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