Abstract
Credit Assignment is an important ingredient of several proposals that have been made for Adaptive Operator Selection. Instead of the average fitness improvement of newborn offspring, this paper proposes to use some empirical order statistics of those improvements, arguing that rare but highly beneficial jumps matter as much or more than frequent but small improvements. An extreme value based Credit Assignment is thus proposed, rewarding each operator with the best fitness improvement observed in a sliding window for this operator. This mechanism, combined with existing Adaptive Operator Selection rules, is investigated in an EC-like setting. First results show that the proposed method allows both the Adaptive Pursuit and the Dynamic Multi-Armed Bandit selection rules to actually track the best operators along evolution.
This is a preview of subscription content, log in via an institution to check access.
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
Lobo, F., Lima, C., Michalewicz, Z. (eds.): Parameter Setting in Evolutionary Algorithms. Springer, Heidelberg (2007)
Birattari, M., Stützle, 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)
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)
Bartz-Beielstein, T., Lasarczyk, C., Preuss, M.: Sequential parameter optimization. In: Proc. CEC 2005, IEEE Press, pp. 773–780. IEEE Press, Los Alamitos (2005)
Nannen, V., Eiben, A.E.: Relevance estimation and value calibration of evolutionary algorithm parameters. In: Proc. IJCAI 2007, Hyderabad, India, pp. 975–980 (2007)
De Jong, K.: Parameter Setting in EAs: a 30 Year Perspective. In: [1], pp. 1–18
Goldberg, D.: Probability Matching, the Magnitude of Reinforcement, and Classifier System Bidding. Machine Learning 5(4), 407–426 (1990)
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)
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)
Davis, L.: Adapting operator probabilities in genetic algorithms. In: Schaffer, J.D. (ed.) Proc. ICGA 1989, pp. 61–69. Morgan Kaufmann, San Francisco (1989)
Lobo, F., Goldberg, D.: Decision making in a hybrid genetic algorithm. In: Proc. ICEC 1997, pp. 121–125. IEEE Press, Los Alamitos (1997)
Tuson, A., Ross, P.: Adapting operator settings in genetic algorithms. Evolutionary Computation 6(2), 161–184 (1998)
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)
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)
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)
Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multiarmed bandit problem. Machine Learning 47(2/3), 235–256 (2002)
Page, E.: Continuous inspection schemes. Biometrika 41, 100–115 (1954)
Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation 9(2), 159–195 (2001)
Gelly, S., Ruette, S., Teytaud, O.: Comparison-based algorithms are robust and randomized algorithms are anytime. Evolutionary Comp. 15(4), 411–434 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fialho, Á., Da Costa, L., Schoenauer, M., Sebag, M. (2008). Extreme Value Based Adaptive Operator Selection. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds) Parallel Problem Solving from Nature – PPSN X. PPSN 2008. Lecture Notes in Computer Science, vol 5199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87700-4_18
Download citation
DOI: https://doi.org/10.1007/978-3-540-87700-4_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87699-1
Online ISBN: 978-3-540-87700-4
eBook Packages: Computer ScienceComputer Science (R0)