Off-line vs. On-line tuning: A study on MAX - MIN ant system for the TSP

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6234 LNCS, Issue , 2010, Pages 239-250

  Pellegrini, Paola ^a   Stützle, Thomas ^b   Birattari, Mauro ^b  

^a CA' FOSCARI UNIVERSITY OF VENICE   (Italy)

^b UNIVERSITÉ LIBRE DE BRUXELLES   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

ANT COLONY OPTIMIZATION ALGORITHMS; ANT SYSTEMS; EXPERIMENTAL CONDITIONS; EXPERIMENTAL STUDIES; MAX-MIN; MAX-MIN ANT SYSTEM; ON-LINE PARAMETER; ONLINE TUNING; PARAMETER ADAPTATION; PARAMETER SETTING; PARAMETER-TUNING; STOCHASTIC LOCAL SEARCHES;

ARTIFICIAL INTELLIGENCE; CELLULAR AUTOMATA; TUNING;

TRAVELING SALESMAN PROBLEM;

EID: 78049304013     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15461-4_21     Document Type: Conference Paper

References (24)

1. Adenso-Díaz, B., Laguna, M.: Fine-tuning of algorithms using fractional experimental designs and local search. Operations Research 54(1), 99-114 (2006) (Pubitemid 43296744)
2. Anghinolfi, D., Boccalatte, A., Paolucci, M., Vecchiola, C.: Performance evaluation of an adaptive ant colony optimization applied to single machine scheduling. In: Li, X., Kirley, M., Zhang, M., Green, D., Ciesielski, V., Abbass, H. A., Michalewicz, Z., Hendtlass, T., Deb, K., Tan, K. C., Branke, J., Shi, Y. (eds.) SEAL 2008. LNCS, vol. 5361, pp. 411-420. Springer, Heidelberg (2008)
3. Balaprakash, P., Birattari, M., Stützle, T.: Improvement strategies for the F-race algorithm: Sampling design and iterative refinement. In: Bartz-Beielstein, T., et al. (eds.) HM 2007. LNCS, vol. 4771, pp. 108-122. Springer, Heidelberg (2007)
4. Battiti, R., Brunato, M., Mascia, F.: Reactive Search and Intelligent Optimization. Operations Research/Computer Science Interfaces, vol. 45. Springer, Berlin (2008)
5. Birattari, M.: Race. R package (2003), http://cran. r-project.org
6. Birattari, M.: On the estimation of the expected performance of a metaheuristic on a class of instances. How many instances, how many runs? Tech. Rep. TR/IRIDIA/2004-01, IRIDIA, Université Libre de Bruxelles, Brussels, Belgium (2004)
7. Birattari, M.: Tuning Metaheuristics: A Machine Learning Perspective. Springer, Berlin (2009)
8. Birattari, M., Dorigo, M.: How to assess and report the performance of a stochastic algorithm on a benchmark problem: Mean or best result on a number of runs? Optimization Letters 1(3), 309-311 (2007) (Pubitemid 46781990)
9. 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)
10. Coy, S., Golden, B., Runger, G., Wasil, E.: Using experimental design to find effective parameter settings for heuristics. Journal of Heuristics 7(1), 77-97 (2001) (Pubitemid 32134485)
11. Dorigo, M., Gambardella, L. M.: Ant Colony System: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1(1), 53-66 (1997) (Pubitemid 127771018)
12. Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)
13. Eiben, A. E., Michalewicz, Z., Schoenauer, M., Smith, J. E.: Parameter control in evolutionary algorithms. In: [19], pp. 19-46
14. Förster, M., Bickel, B., Hardung, B., Kókai, G.: Self-adaptive ant colony optimisation applied to function allocation in vehicle networks. In: GECCO 2007, pp. 1991-1998. ACM Press, New York (2007) (Pubitemid 47291836)
15. Hoos, H. H., Stützle, T.: Stochastic Local Search-Foundations and Applications. Morgan Kaufmann Publishers, San Francisco (2005)
16. Hutter, F., Hoos, H. H., Leyton-Brown, K., Stützle, T.: ParamILS: An automatic algorithm configuration framework. Journal of Artificial Intelligence Research 36, 267-306 (2009)
17. Johnson, D., McGeoch, L., Rego, C., Glover, F.: 8th DIMACS implementation challenge (2001), http://www.research.att.com/~dsj/chtsp/
18. Khichane, M., Albert, P., Solnon, C.: A reactive framework for ant colony optimization. In: Stützle, T. (ed.) LION 3. LNCS, vol. 5851, pp. 119-133. Springer, Heidelberg (2009)
19. Lobo, F., Lima, C. F., Michalewicz, Z.: Parameter Setting in Evolutionary Algorithms. Springer, Berlin (2007)
20. 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) (Pubitemid 47527996)
21. Pellegrini, P., Stützle, T., Birattari, M.: Companion of off-line and on-line tuning: a study on MAX-MIN Ant System for TSP (2010) IRIDIA Supplementary page, http://iridia.ulb.ac.be/supp/IridiaSupp2010-008/
22. Randall, M.: Near Parameter Free Ant Colony Optimisation. In: Dorigo, M., Birattari, M., Blum, C., Gambardella, L. M., Mondada, F., Stützle, T. (eds.) ANTS 2004. LNCS, vol. 3172, pp. 374-381. Springer, Heidelberg (2004) (Pubitemid 39210733)
23. Stützle, T.: ACOTSP: A software package of various ant colony optimization algorithms applied to the symmetric traveling salesman problem (2002), http://www.aco-metaheuristic.org/aco-code
24. Stützle, T., Hoos, H. H.: MAX-MIN ant system. Future Generation Computer Systems 16(8), 889-914 (2000)