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Annual Conference on Artificial Intelligence

KI 2011: KI 2011: Advances in Artificial Intelligence pp 169–178Cite as

Variance Scaling for EDAs Revisited

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7006))

Abstract

Estimation of distribution algorithms (EDAs) are derivative-free optimization approaches based on the successive estimation of the probability density function of the best solutions, and their subsequent sampling. It turns out that the success of EDAs in numerical optimization strongly depends on scaling of the variance. The contribution of this paper is a comparison of various adaptive and self-adaptive variance scaling techniques for a Gaussian EDA. The analysis includes: (1) the Gaussian EDA without scaling, but different selection pressures and population sizes, (2) the variance adaptation technique known as Silverman’s rule-of-thumb, (3) σ-self-adaptation known from evolution strategies, and (4) transformation of the solution space by estimation of the Hessian. We discuss the results for the sphere function, and its constrained counterpart.

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Authors and Affiliations

  1. Department Informatik, Carl von Ossietzky Universität Oldenburg, 26111, Oldenburg, Germany

    Oliver Kramer & Fabian Gieseke

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  1. Oliver Kramer
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  2. Fabian Gieseke
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Editors and Affiliations

  1. Berlin School of Mind and Brain, Humboldt-University of Berlin, Unter den Linden 6, 10099, Berlin, Germany

    Joscha Bach

  2. TZI-Technologie-Zentrum Informatik und Informationstechnik, University of Bremen, Postfach 33 04 40, 28334, Bremen, Germany

    Stefan Edelkamp

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Kramer, O., Gieseke, F. (2011). Variance Scaling for EDAs Revisited. In: Bach, J., Edelkamp, S. (eds) KI 2011: Advances in Artificial Intelligence. KI 2011. Lecture Notes in Computer Science(), vol 7006. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24455-1_16

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  • DOI: https://doi.org/10.1007/978-3-642-24455-1_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24454-4

  • Online ISBN: 978-3-642-24455-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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