Analysing the Adaptation Level of Parallel Hyperheuristics Applied to Mono-objective Optimisation Problems

  • Eduardo Segredo
  • Carlos Segura
  • Coromoto León
Part of the Studies in Computational Intelligence book series (SCI, volume 387)


Evolutionary Algorithms (eas) are one of the most popular strategies for solving optimisation problems. One of the main drawbacks of eas is the complexity of their parameter setting. This setting is mandatory to obtain high quality solutions. In order to deal with the parameterisation of an ea, hyperheuristics can be applied. They manage the choice of which parameters should be applied at each stage of the optimisation process. In this work, an analysis of the robustness of a parallel strategy that hybridises hyperheuristics, and parallel island-based models has been performed. Specifically, the model has been applied to a large set of mono-objective scalable benchmark problems with different landscape features. In addition, a study of the adaptation level of the proposal has been carried out. Computational results have shown the suitability of the model with every tested benchmark problem.


Choice Function Adaptation Level Solve Optimisation Problem Uniform Mutation Polynomial Mutation 
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.


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  1. 1.
    Alba, E.: Parallel Metaheuristics: A New Class of Algorithms. Wiley Interscience, Hoboken (2005)zbMATHCrossRefGoogle Scholar
  2. 2.
    Araya, I., Neveu, B., Riff, M.C.: An Efficient Hyperheuristic for Strip-Packing Problems. In: Cotta, C., Sörensen, K. (eds.) Adaptive and Multilevel Metaheuristics. SCI, vol. 136, pp. 61–76. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Burke, E., Kendall, G., Silva, J.L., O’Brien, R., Soubeiga, E.: An Ant Algorithm Hyperheuristic for the Project Presentation Scheduling Problem. In: Proceedings of the 2005 IEEE Congress on Evolutionary Computation (CEC 2005), Edinburgh, Scotland, vol. 3, pp. 2263–2270 (2005)Google Scholar
  4. 4.
    Burke, E.K., Kendall, G., Newall, J., Hart, E., Ross, P., Schulenburg, S.: Handbook of Meta-heuristics. In: Hyper-heuristics: An Emerging Direction in Modern Search Technology. Kluwer, Dordrecht (2003a)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., McCollum, B., Meisels, A., Petrovic, S., Qu, R.: A graph-based hyper-heuristic for educational timetabling problems. European Journal of Operational Research 176(1), 177–192 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Chen, P.C., Kendall, G., Vanden Berghe, G.: An Ant Based Hyper-heuristic for the Travelling Tournament Problem. In: Proceedings of IEEE Symposium of Computational Intelligence in Scheduling (CISched 2007), Honolulu, Hawaii, pp. 19–26 (2007)Google Scholar
  8. 8.
    Coello, C.A., Lamont, G.B., Veldhuizen, D.A.V.: Evolutionary Algorithms for Solving Multi-Objective Problems. In: Genetic and Evolutionary Computation (2007)Google Scholar
  9. 9.
    Cowling, P., Kendall, G., Soubeiga, E.: A parameter-free hyperheuristic for scheduling a sales summit. In: Proceedings of 4th Metahuristics International Conference (MIC 2001), Porto, Portugal, pp. 127–131 (2001)Google Scholar
  10. 10.
    Cowling, P., Kendall, G., Han, L.: An Investigation of a Hyperheuristic Genetic Algorithm Applied to a Trainer Scheduling Problem. In: Proceedings of the 2002 IEEE Congress on Evolutionary Computation (CEC 2002), pp. 1185–1190. IEEE Computer Society, Honolulu (2002)Google Scholar
  11. 11.
    Cowling, P.I., Kendall, G., Soubeiga, E.: Hyperheuristics: A Robust Optimisation Method Applied to Nurse Scheduling. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 851–860. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    De Jong, K.: Parameter Setting in EAs: a 30 Year Perspective. In: Lobo, F., Lima, C., Michalewicz, Z. (eds.) Parameter Setting in Evolutionary Algorithms, pp. 1–18. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics-Part B 26(1), 29–41 (1996)CrossRefGoogle Scholar
  14. 14.
    Dowsland, K., Soubeiga, E., Burke, E.: A Simulated Annealing Hyper-heuristic for Determining Shipper Sizes. European Journal of Operational Research 179(3), 759–774 (2007)zbMATHCrossRefGoogle Scholar
  15. 15.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Natural Computing Series. Springer, Heidelberg (2008)Google Scholar
  16. 16.
    Glover, F.W., Kochenberger, G.A.: Handbook of Metaheuristics. International Series in Operations Research & Management Science. Springer, Heidelberg (2003)zbMATHGoogle Scholar
  17. 17.
    Hoos, H., Informatik, F., Hoos, H.H., Stutzle, T., Stutzle, T., Intellektik, F., Intellektik, F.: On the Run-time Behavior of Stochastic Local Search Algorithms for SAT. In: Proceedings AAAI 1999, pp. 661–666 (1999)Google Scholar
  18. 18.
    Kendall, G., Cowling, P., Soubeiga, E.: Choice function and random hyperheuristics. In: Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution And Learning (SEAL 2002), Singapore, pp. 667–671 (2002)Google Scholar
  19. 19.
    León, C., Miranda, G., Segura, C.: METCO: A Parallel Plugin-Based Framework for Multi-Objective Optimization. International Journal on Artificial Intelligence Tools 18(4), 569–588 (2009)CrossRefGoogle Scholar
  20. 20.
    Lozano, M., Molina, D., Herrera, F.: Editorial Scalability of Evolutionary Algorithms and Other Metaheuristics for Large-scale Continuous Optimization Problems. In: Soft Computing - A Fusion of Foundations, Methodologies and Applications, pp. 1–3 (2010)Google Scholar
  21. 21.
    Ong, Y.S., Lim, M.H., Zhu, N., Wong, K.W.: Classification of Adaptive Memetic Algorithms: A Comparative Study. IEEE Transactions on Systems, Man, and Cybernetics - Part B 36(1), 141–152 (2006)CrossRefGoogle Scholar
  22. 22.
    Segura, C., Miranda, G., León, C.: Parallel Hyperheuristics for the Frequency Assignment Problem. In: Memetic Computing, pp. 1–17 (2010)Google Scholar
  23. 23.
    Vink, T., Izzo, D.: Learning the best combination of solvers in a distributed global optimization environment. In: Proceedings of Advances in Global Optimization: Methods and Applications (AGO), Mykonos, Greece, pp. 13–17 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Eduardo Segredo
    • 1
  • Carlos Segura
    • 1
  • Coromoto León
    • 1
  1. 1.Dpto. Estadística, I. O. y ComputaciónUniversidad de La LagunaLa LagunaSpain

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