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European Conference on the Applications of Evolutionary Computation

EvoApplications 2014: Applications of Evolutionary Computation pp 603–614Cite as

Noisy Optimization: Convergence with a Fixed Number of Resamplings

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8602))

Abstract

It is known that evolution strategies in continuous domains might not converge in the presence of noise [3, 14]. It is also known that, under mild assumptions, and using an increasing number of resamplings, one can mitigate the effect of additive noise [4] and recover convergence. We show new sufficient conditions for the convergence of an evolutionary algorithm with constant number of resamplings; in particular, we get fast rates (log-linear convergence) provided that the variance decreases around the optimum slightly faster than in the so-called multiplicative noise model.

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Author information

Authors and Affiliations

  1. TAO (Inria), LRI, UMR 8623 (CNRS - Univ. Paris-Sud), Orsay, France

    Marie-Liesse Cauwet

Authors
  1. Marie-Liesse Cauwet
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Correspondence to Marie-Liesse Cauwet .

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Editors and Affiliations

  1. Depto. Estadística e Investigación, Universidad Politécnica de Valencia, Valencia, Spain

    Anna I. Esparcia-Alcázar

  2. University of Granada, Granada, Spain

    Antonio M. Mora

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Cauwet, ML. (2014). Noisy Optimization: Convergence with a Fixed Number of Resamplings. In: Esparcia-Alcázar, A., Mora, A. (eds) Applications of Evolutionary Computation. EvoApplications 2014. Lecture Notes in Computer Science(), vol 8602. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45523-4_49

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  • DOI: https://doi.org/10.1007/978-3-662-45523-4_49

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45522-7

  • Online ISBN: 978-3-662-45523-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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