Abstract
Tuning methods for selecting appropriate parameter configurations of optimization algorithms have been the object of several recent studies. The selection of the appropriate configuration may strongly impact on the performance of evolutionary algorithms. In this paper, we study the performance of three memetic algorithms for the quadratic assignment problem when their parameters are tuned either off-line or on-line. Off-line tuning selects a priori one configuration to be used throughout the whole run for all the instances to be tackled. On-line tuning selects the configuration during the solution process, adapting parameter settings on an instance-per-instance basis, and possibly to each phase of the search. The results suggest that off-line tuning achieves a better performance than on-line tuning.
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References
Birattari, M., Stützle, T., Paquete, L., Varrentrapp, K.: A racing algorithm for configuring metaheuristics. In: Langdon, W., et al. (eds.) GECCO 2002, pp. 11–18. Morgan Kaufmann Publishers, San Francisco (2002)
Balaprakash, P., Birattari, M., Stützle, T.: Improvement strategies for the F-race algorithm: Sampling design and iterative refinement. In: Bartz-Beielstein, T., Blesa Aguilera, M.J., Blum, C., Naujoks, B., Roli, A., Rudolph, G., Sampels, M. (eds.) HM 2007. LNCS, vol. 4771, pp. 108–122. Springer, Heidelberg (2007)
Adenso-Díaz, B., Laguna, M.: Fine-tuning of algorithms using fractional experimental designs and local search. Operations Research 54(1), 99–114 (2006)
Hutter, F., Hoos, H.H., Leyton-Brown, K., Stützle, T.: ParamILS: An automatic algorithm configuration framework. J. Artif. Intell. Res. (JAIR) 36, 267–306 (2009)
Battiti, R., Brunato, M., Mascia, F.: Reactive Search and Intelligent Optimization. Operations Research/Computer Science Interfaces, vol. 45. Springer, Berlin (2008)
Martens, D., Backer, M.D., Haesen, R., Vanthienen, J., Snoeck, M., Baesens, B.: Classification with ant colony optimization. IEEE Transactions on Evolutionary Computation 11(5), 651–665 (2007)
Maturana, J., Fialho, A., Saubion, F., Schoenauer, M., Sebag, M.: Extreme compass and dynamic multi-armed bandits for adaptive operator selection. In: IEEE Congress on Evolutionary Computation, pp. 365–372 (2009)
Eiben, A.E., Michalewicz, Z., Schoenauer, M., Smith, J.E.: Parameter control in evolutionary algorithms. In: Lobo, F., Lima, C.F., Michalewicz, Z. (eds.) Parameter Setting in Evolutionary Algorithms, pp. 19–46. Springer, Berlin (2007)
Whitacre, J.M., Pham, Q.T., Sarker, R.A.: Credit assignment in adaptive evolutionary algorithms. In: Cattolico (ed.) GECCO 2006, pp. 1353–1360. ACM, New York (2006)
Fialho, A.: Adaptive Operator Selection for Optimization. PhD thesis, Université Paris-Sud XI, Orsay, France (2010)
Pellegrini, P., Stützle, T., Birattari, M.: Off-line and on-line tuning: a study on MAX-MIN ant system for TSP. In: Dorigo, M., Birattari, M., Di Caro, G.A., Doursat, R., Engelbrecht, A.P., Floreano, D., Gambardella, L.M., Groß, R., Şahin, E., Sayama, H., Stützle, T. (eds.) ANTS 2010. LNCS, vol. 6234, pp. 239–250. Springer, Heidelberg (2010)
Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Technical Report Caltech Concurrent Computation Program, 826, California Institute of Technology, Pasadena, CA, USA, 1989.
Merz, P., Freisleben, B.: Fitness landscape analysis and memetic algorithms for the quadratic assignment problem. IEEE Transactions on Evolutionary Computation 4(4), 337–352 (2000)
Merz, P., Freisleben, B.: A comparison of memetic algorithms, tabu search, and ant colonies for the quadratic assignment problem. In: Proc. Congress on Evolutionary Computation, pp. 2063–2070. IEEE Press, Los Alamitos (1999)
Merz, P., Freisleben, B.: A genetic local search approach to the quadratic assignment problem. In: 7th International Conference on Genetic Algorithms, pp. 465–472. Morgan Kaufmann, San Francisco (1997)
Goldberg, D.E.: Genetic algorithms and rule learning in dynamic system control. In: International Conference on Genetic Algorithms and Their Applications, pp. 8–15. Morgan Kaufmann Publishers Inc., San Francisco (1985)
Davis, L.: Applying adaptive algorithms to epistatic domains. In: Proc. of IJCAI, pp. 162–164 (1985)
Corne, D.W., Oates, M.J., Kell, D.B.: On fitness distributions and expected fitness gain of mutation rates in parallel evolutionary algorithms. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN VII. LNCS, vol. 2439, pp. 132–141. Springer, Heidelberg (2002)
Thierens, D.: An adaptive pursuit strategy for allocating operator probabilities. In: IEEE Congress on Evolutionary Computation, pp. 1539–1546. IEEE Press, Piscataway (2005)
Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multiarmed bandit problem. Machine Learning 47(2), 235–256 (2002)
Stützle, T., Hoos, H.H.: MAX–MIN ant system. Future Generation Computer Systems 16(8), 889–914 (2000)
Burkard, R., Karisch, S., Rendl, F.: QAPLIB – A quadratic assignment problem library. Journal of Global Optimization (10), 391–403 (1997)
Stützle, T., Fernandes, S.: New benchmark instances for the QAP and the experimental analysis of algorithms. In: Gottlieb, J., Raidl, G. (eds.) EvoCOP 2004. LNCS, vol. 3004, pp. 199–209. Springer, Heidelberg (2004)
Chiarandini, M.: Stochastic Local Search Methods for Highly Constrained Combinatorial Optimisation Problems, ch. 3. PhD thesis, Computer Science Department, Darmstadt University of Technology, Darmstadt, Germany (2005)
Francesca, G., Pellegrini, P., Stützle, T., Birattari, M.: Companion to Off-line and On-line Tuning: a study on operator selection for a memetic algorithm applied to the QAP (2010), http://iridia.ulb.ac.be/supp/IridiaSupp2010-015/ , IRIDIA Supplementary page.
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Francesca, G., Pellegrini, P., Stützle, T., Birattari, M. (2011). Off-line and On-line Tuning: A Study on Operator Selection for a Memetic Algorithm Applied to the QAP. In: Merz, P., Hao, JK. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2011. Lecture Notes in Computer Science, vol 6622. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20364-0_18
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DOI: https://doi.org/10.1007/978-3-642-20364-0_18
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