Hill Climbers and Mutational Heuristics in Hyperheuristics

  • Ender Özcan
  • Burak Bilgin
  • Emin Erkan Korkmaz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4193)


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.


Candidate Solution Choice Function Benchmark Function Hill Climber Heuristic Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ackley, D.: An Empirical Study of Bit Vector Function Optimization. Genetic Algorithms and Simulated Annealing, 170–215 (1987)Google Scholar
  2. 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. 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. 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. 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. 6.
    Burke, E.K., Petrovic, S., Qu, R.: Case Based Heuristic Selection for Timetabling Problems. Journal of Scheduling 9(2), 115–132 (2006)MATHCrossRefGoogle Scholar
  7. 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. 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. 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. 10.
    Easom, E.E.: A Survey of Global Optimization Techniques. M. Eng. thesis, Univ. Louisville, Louisville, KY (1990)Google Scholar
  11. 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. 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. 13.
    Goldberg, D.E.: Genetic Algorithms and Walsh Functions: Part I, A Gentle Introduction. Complex Systems, 129–152 (1989)Google Scholar
  14. 14.
    Goldberg, D.E.: Genetic Algorithms and Walsh Functions: Part II, Deception and Its Analysis. Complex Systems, 153–171 (1989)Google Scholar
  15. 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. 16.
    Griewangk, A.O.: Generalized Descent of Global Optimization. Journal of Optimization Theory and Applications 34, 11–39 (1981)CrossRefMathSciNetGoogle Scholar
  17. 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. 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. 19.
    Kitano, H.: Designing Neural Networks Using Genetic Algorithms with Graph Generation Systems. Complex Systems 4(4), 461–476 (1990)MATHGoogle Scholar
  20. 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. 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. 22.
    Rastrigin, L.A.: Extremal Control Systems. Theoretical Foundations of Engineering Cybernetics Series, Moscow, Nauka, Russian (1974)Google Scholar
  23. 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. 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)MATHGoogle Scholar
  25. 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. 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

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ender Özcan
    • 1
  • Burak Bilgin
    • 1
  • Emin Erkan Korkmaz
    • 1
  1. 1.Department of Computer EngineeringYeditepe UniversityKadıköy/İstanbulTurkey

Personalised recommendations