Abstract
Tuning stochastic local search algorithms for tackling large instances is difficult due to the large amount of CPU-time that testing algorithm configurations requires on such large instances. We define an experimental protocol that allows tuning an algorithm on small tuning instances and extrapolating from the obtained configurations a parameter setting that is suited for tackling large instances. The key element of our experimental protocol is that both the algorithm parameters that need to be scaled to large instances and the stopping time that is employed for the tuning instances are treated as free parameters. The scaling law of parameter values, and the computation time limits on the small instances are then derived through the minimization of a loss function. As a proof of concept, we tune an iterated local search algorithm and a robust tabu search algorithm for the quadratic assignment problem.
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References
Hoos, H.H.: Programming by optimization. Commun. ACM 55(2), 70–80 (2012)
Lourenço, H.R., Martin, O., Stützle, T.: Iterated local search: framework and applications. In: Gendreau, M., Potvin, J.Y. (eds.) Handbook of Metaheuristics. International Series in Operations Research & Management Science, 2nd edn, pp. 363–397. Springer, New York (2010)
Stützle, T.: Iterated local search for the quadratic assignment problem. Eur. J. Oper. Res. 174(3), 1519–1539 (2006)
Taillard, É.D.: Robust taboo search for the quadratic assignment problem. Parallel Comput. 17(4–5), 443–455 (1991)
Koopmans, T.C., Beckmann, M.J.: Assignment problems and the location of economic activities. Econometrica 25, 53–76 (1957)
Taillard, E.D.: Comparison of iterative searches for the quadratic assignment problem. Location Sci. 3(2), 87–105 (1995)
Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evol. Comput. 9(2), 159–195 (2001)
Tseng, L.Y., Chen, C.: Multiple trajectory search for large scale global optimization. In: Proceedings of IEEE Congress on Evolutionary Computation, Piscataway, NJ, IEEE, pp. 3052–3059 June 2008
Birattari, M., Zlochin, M., Dorigo, M.: Towards a theory of practice in metaheuristics design: a machine learning perspective. Theor. Inform. Appl. 40(2), 353–369 (2006)
Mladenovic, N., Hansen, P.: Variable neighbourhood search. Comput. Oper. Res. 24(11), 71–86 (1997)
Hansen, P., Mladenovic, N.: Variable neighborhood search: principles and applications. Eur. J. Oper. Res. 130(3), 449–467 (2001)
Styles, J., Hoos, H.H., Müller, M.: Automatically configuring algorithms for scaling performance. In: Hamadi, Y., Schoenauer, M. (eds.) LION 6. LNCS, vol. 7219, pp. 205–219. Springer, Heidelberg (2012)
Acknowledgments
This work was supported by the META-X project, an Action de Recherche Concertée funded by the Scientific Research Directorate of the French Community of Belgium. Franco Mascia, Mauro Birattari, and Thomas Stützle acknowledge support from the Belgian F.R.S.-FNRS. The authors also acknowledge support from the FRFC project “Méthodes de recherche hybrids pour la résolution de problèmes complexes”. This research and its results have also received funding from the COMEX project within the Interuniversity Attraction Poles Programme of the Belgian Science Policy Office.
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Mascia, F., Birattari, M., Stützle, T. (2013). Tuning Algorithms for Tackling Large Instances: An Experimental Protocol. In: Nicosia, G., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 2013. Lecture Notes in Computer Science(), vol 7997. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44973-4_44
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DOI: https://doi.org/10.1007/978-3-642-44973-4_44
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