Abstract
Surrogate ranking in evolutionary computation using ordinal regression is introduced. The fitness of individual points is indirectly estimated by modeling their rank. The aim is to reduce the number of costly fitness evaluations needed for evolution. The ordinal regression, or preference learning, implements a kernel-defined feature space and an optimization technique by which the margin between rank boundaries is maximized. The technique is illustrated on some classical numerical optimization functions using an evolution strategy. The benefits of surrogate ranking, compared to surrogates that model the fitness function directly, are discussed.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ong, Y., Nair, P., Keane, A., Wong, K.W.: 15. Studies in Fuzziness and Soft Computing Series. In: Surrogate-Assisted Evolutionary Optimization Frameworks for High-Fidelity Engineering Design Problems, pp. 333–358. Springer, Heidelberg (2004)
Sobester, A., Leary, S., Keane, A.: On the design of optimization strategies based on global response surface approximation models. Journal of Global Optimization 33(1), 31–59 (2005)
Jin, Y.: A comprehensive survey of fitness approximation in evolutionary computation. Soft Computing - A Fusion of Foundations, Methodologies and Applications 9(1), 3–12 (2005)
Jin, Y., Olhofer, M., Sendhoff, B.: A framework for evolutionary optimization with approximate fitness functions. IEEE Transactions on Evolutionary Computation 6(5) (2002)
Bandler, J., Cheng, Q., Dakroury, S., Mohamed, A., Bakr, M., Madsen, K., Sondergaard, J.: Space mapping: The state of the art. IEEE Transactions on Microwave Theory and Techniques 52(1) (2004)
Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation 9(2), 159–195 (2001)
Herbrich, R., Graepel, T., Obermayer, K.: Large margin rank boundaries for ordinal regression. Advances in Large Margin Classifiers, 115–132 (2000)
Joachims, T.: Optimizing search engines using clickthrough data. In: Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), ACM, New York (2002)
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)
Christianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2002)
Runarsson, T.P.: Constrained evolutionary optimization by approximate ranking and surrogate models. In: Parallel Problem Solving from Nature VII (PPSN 2004). LNCS, vol. 3242, pp. 401–410. Springer, Heidelberg (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Runarsson, T.P. (2006). Ordinal Regression in Evolutionary Computation. In: Runarsson, T.P., Beyer, HG., Burke, E., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds) Parallel Problem Solving from Nature - PPSN IX. PPSN 2006. Lecture Notes in Computer Science, vol 4193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11844297_106
Download citation
DOI: https://doi.org/10.1007/11844297_106
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38990-3
Online ISBN: 978-3-540-38991-0
eBook Packages: Computer ScienceComputer Science (R0)