Extreme value based adaptive operator selection

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

Volumn 5199 LNCS, Issue , 2008, Pages 175-184

  Fialho, Álvaro ^a   Da Costa, Luís ^b   Schoenauer, Marc ^a,b   Sebag, Michèle ^a,b  

^a Parc Orsay Université   (France)

^b INRIA SACLAY   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE OPERATORS; BEST FITNESSES; CREDIT ASSIGNMENTS; EXTREME VALUES; ORDER STATISTICS; SELECTION RULES; SLIDING WINDOWS;

PROBLEM SOLVING;

ARTIFICIAL INTELLIGENCE; COMPUTERS;

ADAPTIVE OPERATOR SELECTIONS; CREDIT ASSIGNMENT; EXTREME VALUE; MULTI ARMED BANDIT; ORDER STATISTICS; SELECTION RULES; SLIDING WINDOW;

EID: 56449098210     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-87700-4_18     Document Type: Conference Paper

References (19)

1. Lobo, F., Lima, C., Michalewicz, Z. (eds.): Parameter Setting in Evolutionary Algorithms. Springer, Heidelberg (2007)
2. Birattari, M., Stutzle, T., Paquete, L., Varrentrapp, K.: A racing algorithm for configuring metaheuristics. in: Langdon, W.B., et al. (eds.) Proc. GECCO 2002, pp. 11-18. Morgan Kaufmann, San Francisco (2002)
3. Yuan, B., Gallagher, M.: Statistical racing techniques for improved empirical evaluation of evolutionary algorithms, in: Yao, X., et al. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 172-181. Springer, Heidelberg (2004)
4. Bartz-Beielstein, T., Lasarczyk, C., Preuss, M.: Sequential parameter optimization, in: Proc. CEC 2005, IEEE Press, pp. 773-780. IEEE Press, Los Alamitos (2005)
5. Nannen, V., Eiben, A.E.: Relevance estimation and value calibration of evolutionary algorithm parameters. In: Proc. IJGAI 2007, Hyderabad, India, pp. 975-980 (2007)
6. De Jong, K.: Parameter Setting in EAs: a 30 Year Perspective. in: [1], pp. 1-18
7. Goldberg, D.: Probability Matching, the Magnitude of Reinforcement, and Classifier System Bidding. Machine Learning 5(4), 407-426 (1990)
8. Thierens, D.: An adaptive pursuit, strategy for allocating operator probabilities. In: Beyer, H.G. (ed.) Proc. GECCO 2005, pp. 1539-1546. ACM Press, New York (2005)
9. Da, Costa, L., Fialho, A., Schoenauer, M., Sebag, M.: Adaptive operator selection with dynamic multi-armed bandits. In: Keijzer, M., et al. (eds.) Proc. GECCO 2008, ACM Press, New York (to appear, 2008)
10. Davis, L.: Adapting operator probabilities in genetic algorithms. In: Schaffer, J.D. (ed.) Proc. ICGA 1989, pp. 61-69. Morgan Kaufmann, San Francisco (1989)
11. Lobo, F., Goldberg, D.: Decision making in a hybrid genetic algorithm. In: Proc. ICEC 1997, pp. 121-125. IEEE Press, Los Alamitos (1997)
12. Tuson, A., Ross, P.: Adapting operator settings in genetic algorithms. Evolutionary Computation 6(2), 161-184 (1998)
13. Barbosa, H.J.C., e Sá, A.M.: On adaptive operator probabilities in real coded genetic algorithms. In: XX Intl. Conf. of the Chilean Computer Science Society (2000)
14. Julstrom, B.A.: What have you done for me lately? adapting operator probabilities in a steady-state genetic algorithm on genetic algorithms. In: Eshelman, L.J. (ed.) Proc. ICGA 1995, pp. 81-87. Morgan Kaufmann, San Francisco (1995)
15. Whitacre, J.M., Pham, T.Q., Sarker, R.A.: Use of statistical outlier detection method in adaptive evolutionary algorithms. In: Cattolico, M. (ed.) Proc. GECCO 2006, pp. 1345-1352. ACM, New York (2006)
16. Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multiarmed bandit problem. Machine Learning 47(2/3), 235-256 (2002)
17. Page, E.: Continuous inspection schemes. Biometrika 41, 100-115 (1954)
18. Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation 9(2), 159-195 (2001)
19. Gelly, S., Ruette, S., Teytaud, O.: Comparison-based algorithms are robust and randomized algorithms are anytime. Evolutionary Comp. 15(4), 411-434 (2007)