Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alba, E.: Parallel Metaheuristics: A New Class of Algorithms. Wiley Interscience, Hoboken (2005)
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)
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)
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)
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., 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)
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)
Coello, C.A., Lamont, G.B., Veldhuizen, D.A.V.: Evolutionary Algorithms for Solving Multi-Objective Problems. In: Genetic and Evolutionary Computation (2007)
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)
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)
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)
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)
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)
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)
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Natural Computing Series. Springer, Heidelberg (2008)
Glover, F.W., Kochenberger, G.A.: Handbook of Metaheuristics. International Series in Operations Research & Management Science. Springer, Heidelberg (2003)
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)
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)
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)
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)
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)
Segura, C., Miranda, G., León, C.: Parallel Hyperheuristics for the Frequency Assignment Problem. In: Memetic Computing, pp. 1–17 (2010)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Segredo, E., Segura, C., León, C. (2011). Analysing the Adaptation Level of Parallel Hyperheuristics Applied to Mono-objective Optimisation Problems. In: Pelta, D.A., Krasnogor, N., Dumitrescu, D., Chira, C., Lung, R. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2011). Studies in Computational Intelligence, vol 387. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24094-2_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-24094-2_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24093-5
Online ISBN: 978-3-642-24094-2
eBook Packages: EngineeringEngineering (R0)