Parallel Problem Solving from Nature - PPSN IX pp 202-211 | Cite as
Hill Climbers and Mutational Heuristics in Hyperheuristics
Conference paper
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.
Keywords
Candidate Solution Choice Function Benchmark Function Hill Climber Heuristic Selection
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References
- 1.Ackley, D.: An Empirical Study of Bit Vector Function Optimization. Genetic Algorithms and Simulated Annealing, 170–215 (1987)Google Scholar
- 2.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)Google Scholar
- 3.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)Google Scholar
- 4.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)Google Scholar
- 5.Burke, E.K., Kendall, G., Soubeiga, E.: A Tabu-Search Hyperheuristic for Timetabling and Rostering. Journal of Heuristics 9(6), 451–470 (2003)CrossRefGoogle Scholar
- 6.Burke, E.K., Petrovic, S., Qu, R.: Case Based Heuristic Selection for Timetabling Problems. Journal of Scheduling 9(2), 115–132 (2006)zbMATHCrossRefGoogle Scholar
- 7.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)CrossRefGoogle Scholar
- 8.Davis, L.: Bit Climbing, Representational Bias, and Test Suite Design. In: Proceeding of the 4th International conference on Genetic Algorithms, pp. 18–23 (1991)Google Scholar
- 9.De Jong, K.: An Analysis of the Behaviour of a Class of Genetic Adaptive Systems. PhD thesis, University of Michigan (1975)Google Scholar
- 10.Easom, E.E.: A Survey of Global Optimization Techniques. M. Eng. thesis, Univ. Louisville, Louisville, KY (1990)Google Scholar
- 11.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)Google Scholar
- 12.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)Google Scholar
- 13.Goldberg, D.E.: Genetic Algorithms and Walsh Functions: Part I, A Gentle Introduction. Complex Systems, 129–152 (1989)Google Scholar
- 14.Goldberg, D.E.: Genetic Algorithms and Walsh Functions: Part II, Deception and Its Analysis. Complex Systems, 153–171 (1989)Google Scholar
- 15.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)Google Scholar
- 16.Griewangk, A.O.: Generalized Descent of Global Optimization. Journal of Optimization Theory and Applications 34, 11–39 (1981)CrossRefMathSciNetGoogle Scholar
- 17.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)CrossRefGoogle Scholar
- 18.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)Google Scholar
- 19.Kitano, H.: Designing Neural Networks Using Genetic Algorithms with Graph Generation Systems. Complex Systems 4(4), 461–476 (1990)zbMATHGoogle Scholar
- 20.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)Google Scholar
- 21.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)Google Scholar
- 22.Rastrigin, L.A.: Extremal Control Systems. Theoretical Foundations of Engineering Cybernetics Series, Moscow, Nauka, Russian (1974)Google Scholar
- 23.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)Google Scholar
- 24.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)zbMATHGoogle Scholar
- 25.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)Google Scholar
- 26.Whitley, D.: Fundamental Principles of Deception in Genetic Search. In: Rawlins, G.J.E. (ed.) Foundations of Genetic Algorithms, Morgan Kaufmann, San Matco (1991)Google Scholar
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