Abstract
Given some optimization problem and a series of typically expensive trials of solution candidates taken from a search space, how can we efficiently select the next candidate? We address this fundamental problem using adaptive grids inspired by Kohonen’s self-organizing map. Initially the grid divides the search space into equal simplexes. To select a candidate we uniform randomly first select a simplex, then a point within the simplex. Grid nodes are attracted by candidates that lead to improved evaluations. This quickly biases the active data selection process towards promising regions, without loss of ability to deal with ”surprising” global optima in other areas. On standard benchmark functions the technique performs more reliably than the widely used covariance matrix adaptation evolution strategy.
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
T. Kohonen, Self-Organizing maps, Springer Verlag 1995
H. P. Schwefel, Evolution and Optimum Seeking,Wiley 1995
T. Bäck, U. Hammel, H. P. Schwefel, “Evolutionary Computation. Comments on the History and Current State”, IEEE Trans. on Evolutionary Computation, vol. 1, n. 1, 1997, pp. 3–17
N. Hansen, A. Ostermeier, “Adapting Arbitrary Normal Mutation Distributions in Evolution Strategies: The Covariance Matrix Adaptation”, IEEE Intern. Conf. on Evolutionary Computation (ICEC) Proceedings, 1996, pp. 312–317
D. Whitley, K. Mathias, S. Rana, J. Dzubera, “Building Better Test Functions”, Proc. of the 6th Int. Conf. on GAs, Morgan Kaufmann, 1995, pp. 239–246
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Milano, M., Schmidhuber, J., Koumoutsakos, P. (2001). Active Learning with Adaptive Grids. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_61
Download citation
DOI: https://doi.org/10.1007/3-540-44668-0_61
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42486-4
Online ISBN: 978-3-540-44668-2
eBook Packages: Springer Book Archive