Abstract
Hyper-heuristics are proposed as a higher level of abstraction as compared to the metaheuristics. Hyper-heuristic methods deploy a set of simple heuristics and use only non-problem-specific data, such as fitness change or heuristic execution time. A typical iteration of a hyper-heuristic algorithm consists of two phases: the heuristic selection method and move acceptance. In this paper, heuristic selection mechanisms and move acceptance criteria in hyper-heuristics are analyzed in depth. Seven heuristic selection methods and five acceptance criteria are implemented. The performance of each selection and acceptance mechanism pair is evaluated on 14 well-known benchmark functions and 21 exam timetabling problem instances.
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References
Ackley, D.: An empirical study of bit vector function optimization. In: Davis, L. (ed.) Genetic Algorithms and Simulated Annealing, pp. 170–215. Pitman, London (1987)
Ayob, M., Kendall, G.: A Monte Carlo hyper-heuristic to optimise component placement sequencing for multi head placement machine. In: InTech 2003. Proceedings of the International Conference on Intelligent Technologies, Chiang Mai, Thailand, pp. 132–141 (December 2003)
Burke, E.K., Kendall, G., Newall, J., Hart, E., Ross, P., Schulenburg, S.: Hyper-heuristics: an emerging direction in modern search technology. In: Glover, F., Kochenberger, G.A. (eds.) Handbook of Metaheuristics. International Series in Operations Research and Management Science, vol. 57, pp. 457–474. Kluwer, Dordrecht (2003)
Burke, E., Newall, J.P., Weare, R.F.: A memetic algorithm for university exam timetabling. In: Burke, E.K., Ross, P. (eds.) Practice and Theory of Automated Timetabling. LNCS, vol. 1153, pp. 241–250. Springer, Heidelberg (1996)
Burke, E.K., Kendall, G., Soubeiga, E.: A tabu-search hyper-heuristic for timetabling and rostering. Journal of Heuristics 9, 451–470 (2003)
Burke, E., Elliman, D., Ford, P., Weare, B.: Examination timetabling in British universities – a survey. In: Burke, E.K., Ross, P. (eds.) Practice and Theory of Automated Timetabling. LNCS, vol. 1153, pp. 76–90. Springer, Heidelberg (1996)
Burke, E.K., Newall, J.P.: Solving examination timetabling problems through adaption of heuristic orderings: models and algorithms for planning and scheduling problems. Annals of Operations Research 129, 107–134 (2004)
Burke, E.K., McCollum, B., Meisels, A., Petrovic, S., Qu, R.: A graph-based hyper heuristic for timetabling problems. European Journal of Operational Research 176, 177–192 (2007)
Burke, E.K., Petrovic, S., Qu, R.: Case based heuristic selection for timetabling problems. Journal of Scheduling 9, 115–132 (2006)
Carter, M.W, Laporte, G., Lee, S.T.: Examination timetabling: algorithmic strategies and applications. Journal of the Operational Research Society 47, 373–383 (1996)
Cowling, P., Kendall, G., Soubeiga, E.: A hyper-heuristic approach to scheduling a sales summit. In: Burke, E., Erben, W. (eds.) PATAT 2000. LNCS, vol. 2079, pp. 176–190. Springer, Heidelberg (2001)
Davis, L.: Bit climbing, representational bias, and test suite design. In: Proceedings of the 4th International Conference on Genetic Algorithms, pp. 18–23 (1991)
De Jong, K.: An analysis of the behaviour of a class of genetic adaptive systems. Ph.D. Thesis, University of Michigan (1975)
Di Gaspero, L., Schaerf, A.: Tabu search techniques for examination timetabling. In: Burke, E., Erben, W. (eds.) PATAT 2000. LNCS, vol. 2079, pp. 104–117. Springer, Heidelberg (2001)
Easom, E.E.: A survey of global optimization techniques. M.Eng. Thesis, University of Louisville, KY (1990)
Even, S., Itai, A., Shamir, A.: On the complexity of timetable and multicommodity flow problems. SIAM Journal of Computing 5, 691–703 (1976)
Goldberg, D.E.: Genetic algorithms and Walsh functions: Part I, A gentle introduction. Complex Systems 3, 129–152 (1989)
Goldberg, D.E.: Genetic algorithms and Walsh functions: Part II, Deception and its analysis. Complex Systems 3, 153–171 (1989)
Griewangk, A.O.: Generalized descent of global optimization. Journal of Optimization Theory and Applications 34, 11–39 (1981)
Kendall, G., Mohamad, M.: Channel assignment in cellular communication using a great deluge hyper-heuristic. In: Proceedings of the 2004 IEEE International Conference on Networks, pp. 769–773. IEEE Computer Society Press, Los Alamitos (2004)
Marin, H.T.: Combinations of GAs and CSP strategies for solving examination timetabling problems. Ph.D. Thesis, Instituto Tecnologico y de Estudios Superiores de Monterrey (1998)
Merlot, L.T.G., Boland, N., Hughes, B.D., Stuckey, P.J.: A hybrid algorithm for the examination timetabling problem. In: Burke, E.K., De Causmaecker, P. (eds.) PATAT 2002. LNCS, vol. 2740, pp. 207–231. Springer, Heidelberg (2003)
Mitchell, M., Forrest, S.: Fitness landscapes: Royal Road functions. In: Baeck, T., Fogel, D., Michalewiz, Z. (eds.) Handbook of Evolutionary Computation, Institute of Physics Publishing, Bristol and Oxford University Press, Oxford (1997)
Özcan, E.: Towards an XML based standard for timetabling problems: TTML. In: Multidisciplinary Scheduling: Theory and Applications, vol. 163 (24), Springer, Berlin (2005)
Özcan, E., Ersoy, E.: Final exam scheduler – FES. In: Proceedings of the 2005 IEEE Congress on Evolutionary Computation, vol. 2, pp. 1356–1363 (2005)
Paquete, L.F., Fonseca, C.M.: A study of examination timetabling with multiobjective evolutionary algorithms. In: MIC 2001. Proceedings of the 4th Metaheuristics International Conference, pp. 149–154.
Petrovic, S., Yang, Y., Dror, M.: Case-based initialisation for examination timetabling. In: MISTA 2003. Proceedings of the 1st Multidisciplinary International Conference on Scheduling: Theory and Applications, Nottingham, pp. 137–154 (August 2003)
Rastrigin, L.A.: Extremal Control Systems. Theoretical Foundations of Engineering Cybernetics Series. Nauka, Moscow (1974)
Rattadilok, P., Gaw, A., Kwan, R.S.K.: Distributed choice function hyperheuristics for timetabling and scheduling. In: Burke, E.K., Trick, M.A. (eds.) PATAT 2004. LNCS, vol. 3616, pp. 51–67. Springer, Heidelberg (2005)
Schwefel, H.P.: Numerical Optimization of Computer Models. Wiley, New York (1981) [translation of Numerische Optimierung von Computer-Modellen mittels der Evolutionsstrategie (1977)]
Whitley, D.: Fundamental principles of deception in genetic search. In: Rawlins, G.J.E. (ed.) Foundations of Genetic Algorithms, Morgan Kaufmann, San Mateo, CA (1991)
Wong, T., Côté, P., Gely, P.: Final exam timetabling: a practical approach. In: Proceedings of the IEEE Canadian Conference on Electrical and Computer Engineering, Winnipeg, vol. 2, pp. 726–731 (May 2002)
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Bilgin, B., Özcan, E., Korkmaz, E.E. (2007). An Experimental Study on Hyper-heuristics and Exam Timetabling. In: Burke, E.K., Rudová, H. (eds) Practice and Theory of Automated Timetabling VI. PATAT 2006. Lecture Notes in Computer Science, vol 3867. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77345-0_25
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DOI: https://doi.org/10.1007/978-3-540-77345-0_25
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