Abstract
Hyperheuristics are single candidate solution based and simple to maintain mechanisms used in optimization. At each iteration, as a higher level of abstraction, a hyperheuristic chooses and applies one of the heuristics to a candidate solution. In this study, the performance contribution of hill climbing operators along with the mutational heuristics are analyzed in depth in four different hyperheuristic frameworks. Four different hill climbing operators and three mutational operators are used during the experiments. Various subsets of the heuristics are evaluated on fourteen well-known benchmark functions.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Ackley, D.: An Empirical Study of Bit Vector Function Optimization. Genetic Algorithms and Simulated Annealing, 170–215 (1987)
Ayob, M., Kendall, G.: A Monte Carlo Hyperheuristic To Optimise Component Placement Sequencing For Multi Head Placement Machine. In: Proc. of the Int. Conference on Intelligent Technologies, In Tech 2003, Chiang Mai, Thailand, December 17-19, pp. 132–141 (2003)
Bilgin, B., Ozcan, E., Korkmaz, E.E.: An Experimental Study on Hyper-heuristics and Final Exam Scheduling. In: Proc. of the 6th Int. Conf. on PATAT (to appear, 2006)
Burke, E.K., Kendall, G., Newall, J., Hart, E., Ross, P., Schulenburg, S.: Hyperheuristics: An Emerging Direction in Modern Search Technology. In: Glover, F., Kochenberger, G.A. (eds.) Handbook of Metaheuristics. International Series in OR & Management Science, vol. 57, pp. 457–474. Kluwer Academic Publishers, Boston, Dordrecht, London (2003)
Burke, E.K., Kendall, G., Soubeiga, E.: A Tabu-Search Hyperheuristic for Timetabling and Rostering. Journal of Heuristics 9(6), 451–470 (2003)
Burke, E.K., Petrovic, S., Qu, R.: Case Based Heuristic Selection for Timetabling Problems. Journal of Scheduling 9(2), 115–132 (2006)
Cowling, P., 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)
Davis, L.: Bit Climbing, Representational Bias, and Test Suite Design. In: Proceeding 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. PhD thesis, University of Michigan (1975)
Easom, E.E.: A Survey of Global Optimization Techniques. M. Eng. thesis, Univ. Louisville, Louisville, KY (1990)
Fang, H.-L., Ross, P.M., Corne, D.: A Promising Hybrid GA/Heuristic Approach for Open-Shop Scheduling Problems. In: Proc. of the 11th European Conf. on Artificial Intelligence, pp. 590–594 (1994)
Fisher, H., Thompson, G.L.: Probabilistic Learning Combinations of Local Job-shop Scheduling Rules. In: Factory Scheduling Conf., Carnegie Institute of Tech, May 10-12 (1961)
Goldberg, D.E.: Genetic Algorithms and Walsh Functions: Part I, A Gentle Introduction. Complex Systems, 129–152 (1989)
Goldberg, D.E.: Genetic Algorithms and Walsh Functions: Part II, Deception and Its Analysis. Complex Systems, 153–171 (1989)
Gratch, J., Chein, S., de Jong, G.: Learning Search Control Knowledge for Deep Space Network Scheduling. In: Proc. of 10th Int. Conf. on Machine Learning, pp. 135–142 (1993)
Griewangk, A.O.: Generalized Descent of Global Optimization. Journal of Optimization Theory and Applications 34, 11–39 (1981)
Hart, E., Ross, P.M.: A Heuristic Combination Method for Solving Job-Shop Scheduling Problems. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 845–854. Springer, Heidelberg (1998)
Kendall, G., Mohamad, M.: Channel Assignment in Cellular Communication Using a Great Deluge Hyperheuristic. In: Proc. of the IEEE Int. Conf. on Network, pp. 769–773 (2004)
Kitano, H.: Designing Neural Networks Using Genetic Algorithms with Graph Generation Systems. Complex Systems 4(4), 461–476 (1990)
Mitchell, M., Forrest, S.: Fitness Landscapes: Royal Road Functions. In: Baeck, T., Fogel, D., Michalewiz, Z. (eds.) Handbook of Evolutionary Computation, pp. 1–25. Institute of Physics Publishing and Oxford University (1997)
Ozcan, E.: An Empirical Investigation on Memes, Self-generation and Nurse Rostering. In: Proc. of the 6th Int. Conf. on PATAT 2006 (to appear, 2006)
Rastrigin, L.A.: Extremal Control Systems. Theoretical Foundations of Engineering Cybernetics Series, Moscow, Nauka, Russian (1974)
Ross, P.: Hyperheuristics. In: Burke, E.K., Kendall, G. (eds.) Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, pp. 529–556. Springer, Berlin, Heidelberg, New York (2005)
Schwefel, H.P.: Numerical Optimization of Computer Models. John Wiley & Sons, Chichester (1981); English translation of Numerische Optimierung von Computer-Modellen mittels der Evolutionsstrategie (1977)
Terashima-Marin, H., Ross, P.M., Valenzuela-Rendon, M.: Evolution of Constraint Satisfaction Strategies in Examination Timetabling. In: Proc. of Genetic and Evolutionary Computation Conference – GECCO, pp. 635–642 (1999)
Whitley, D.: Fundamental Principles of Deception in Genetic Search. In: Rawlins, G.J.E. (ed.) Foundations of Genetic Algorithms, Morgan Kaufmann, San Matco (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Özcan, E., Bilgin, B., Korkmaz, E.E. (2006). Hill Climbers and Mutational Heuristics in Hyperheuristics. In: Runarsson, T.P., Beyer, HG., Burke, E., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds) Parallel Problem Solving from Nature - PPSN IX. PPSN 2006. Lecture Notes in Computer Science, vol 4193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11844297_21
Download citation
DOI: https://doi.org/10.1007/11844297_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38990-3
Online ISBN: 978-3-540-38991-0
eBook Packages: Computer ScienceComputer Science (R0)