Abstract
Hyper-heuristics are a class of high-level search technologies to solve computationally difficult problems which operate on a search space of low-level heuristics rather than solutions directly. A iterative selection hyper-heuristic framework based on single-point search relies on two key components, a heuristic selection method and a move acceptance criteria. The Choice Function is an elegant heuristic selection method which scores heuristics based on a combination of three different measures and applies the heuristic with the highest rank at each given step. Each measure is weighted appropriately to provide balance between intensification and diversification during the heuristic search process. Choosing the right parameter values to weight these measures is not a trivial process and a small number of methods have been proposed in the literature. In this study we describe a new method, inspired by reinforcement learning, which controls these parameters automatically. The proposed method is tested and compared to previous approaches over a standard benchmark across six problem domains.
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References
Cowling, P.I., Kendall, G., Soubeiga, E.: A Hyperheuristic Approach to Scheduling a Sales Summit. In: Burke, E., Erben, W. (eds.) PATAT 2000. LNCS, vol. 2079, pp. 176–190. Springer, Heidelberg (2001)
Fisher, M., Thompson, G.: Probabilistic learning combinations of local job-shop scheduling rules. In: Factory Scheduling Conference (1961)
Burke, E.K., Kendall, G., Soubeiga, E.: A tabu-search hyperheuristic for timetabling and rostering. Journal of Heuristics 9, 451–470 (2003)
Özcan, E., Bykov, Y., Birben, M., Burke, E.K.: Examination timetabling using late acceptance hyper-heuristics. In: CEC 2009, pp. 997–1004 (2009)
Özcan, E., Misir, M., Ochoa, G., Burke, E.K.: A reinforcement learning - great-deluge hyper-heuristic for examination timetabling. Int. Journal of Applied Metaheuristic Computing 1, 39–59 (2010)
Burke, E.K., Kendall, G., Misir, M., Özcan, E.: Monte carlo hyper-heuristics for examination timetabling. Annals of Operations Research (in press)
Sabar, N.R., Ayob, M., Qu, R., Kendall, G.: A graph coloring constructive hyper-heuristic for examination timetabling problems. Applied Intelligence (in press)
Ochoa, G., Hyde, M., Curtois, T., Vazquez-Rodriguez, J.A., Walker, J., Gendreau, M., Kendall, G., McCollum, B., Parkes, A.J., Petrovic, S., Burke, E.K.: HyFlex: A Benchmark Framework for Cross-Domain Heuristic Search. In: Hao, J.-K., Middendorf, M. (eds.) EvoCOP 2012. LNCS, vol. 7245, pp. 136–147. Springer, Heidelberg (2012)
López-Camacho, E., Terashima-Marín, H., Ross, P.: A hyper-heuristic for solving one and two-dimensional bin packing problems. In: GECCO 2011, pp. 257–258. ACM, New York (2011)
Gibbs, J., Kendall, G., Özcan, E.: Scheduling English Football Fixtures over the Holiday Period Using Hyper-heuristics. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6238, pp. 496–505. Springer, Heidelberg (2010)
Kiraz, B., Uyar, A.Ş., Özcan, E.: An Investigation of Selection Hyper-heuristics in Dynamic Environments. In: Di Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcázar, A.I., Merelo, J.J., Neri, F., Preuss, M., Richter, H., Togelius, J., Yannakakis, G.N. (eds.) EvoApplications 2011, Part I. LNCS, vol. 6624, pp. 314–323. Springer, Heidelberg (2011)
Garrido, P., Castro, C.: Stable solving of cvrps using hyperheuristics. In: GECCO 2009, pp. 255–262. ACM (2009)
Burke, E.K., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., Woodward, J.: A Classification of Hyper-heuristics Approaches. In: Handbook of Metaheuristics, 2nd edn., pp. 449–468. Springer (2010)
Burke, E.K., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., Qu, R.: Hyper-heuristics: A survey of the state of the art. Technical Report No. NOTTCS-TR-SUB-0906241418-2747, School of Comp. Sci., University of Nottingham (2010)
Özcan, E., Bilgin, B., Korkmaz, E.E.: Hill Climbers and Mutational Heuristics in Hyperheuristics. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN IX. LNCS, vol. 4193, pp. 202–211. Springer, Heidelberg (2006)
Cowling, P., Kendall, G., Soubeiga, E.: A parameter-free hyperheuristic for scheduling a sales summit. In: MIC 2001, pp. 127–131 (2001)
Bilgin, B., Özcan, E., Korkmaz, E.E.: An Experimental Study on Hyper-Heuristics and Exam Timetabling. In: Burke, E.K., Rudová, H. (eds.) PATAT 2006. LNCS, vol. 3867, pp. 394–412. Springer, Heidelberg (2007)
Ochoa, G., Hyde, M.: The cross-domain heuristic search challenge (CHeSC 2011) (2011), http://www.asap.cs.nott.ac.uk/chesc2011/
Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: a survey. Journal of Artificial Intelligence Research 4, 237–285 (1996)
Özcan, E., Bilgin, B., Korkmaz, E.E.: A comprehensive analysis of hyper-heuristics. Intelligent Data Analysis 12(1), 3–23 (2008)
Di Gaspero, L., Urli, T.: Evaluation of a family of reinforcement learning cross-domain heuristics for optimization. In: LION 6 (2012)
Özcan, E., Basaran, C.: A case study of memetic algorithms for constraint optimization. Soft Computing 13(8-9), 871–882 (2009)
Chakhlevitch, K., Cowling, P.I.: Choosing the Fittest Subset of Low Level Heuristics in a Hyperheuristic Framework. In: Raidl, G.R., Gottlieb, J. (eds.) EvoCOP 2005. LNCS, vol. 3448, pp. 23–33. Springer, Heidelberg (2005)
Özcan, E., Kheiri, A.: A hyper-heuristic based on random gradient, greedy and dominance. In: ISCIS 2011, pp. 404–409 (2011)
Drake, J.H., Özcan, E., Burke, E.K.: Controlling crossover in a selection hyper-heuristic framework. Technical Report No. NOTTCS-TR-SUB-1104181638-4244, School of Computer Science, University of Nottingham (2011)
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Drake, J.H., Özcan, E., Burke, E.K. (2012). An Improved Choice Function Heuristic Selection for Cross Domain Heuristic Search. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds) Parallel Problem Solving from Nature - PPSN XII. PPSN 2012. Lecture Notes in Computer Science, vol 7492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32964-7_31
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DOI: https://doi.org/10.1007/978-3-642-32964-7_31
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