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On multiplicative noise models for stochastic search

  • Mohamed Jebalia
  • , Anne Auger
  • INRIA
  • Microsoft Research

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Citations (Scopus)

Abstract

In this paper we investigate multiplicative noise models in the context of continuous optimization. We illustrate how some intrinsic properties of the noise model imply the failure of reasonable search algorithms for locating the optimum of the noiseless part of the objective function. Those findings are rigorously investigated on the (1 + 1)-ES for the minimization of the noisy sphere function. Assuming a lower bound on the support of the noise distribution, we prove that the (1 + 1)-ES diverges when the lower bound allows to sample negative fitness with positive probability and converges in the opposite case. We provide a discussion on the practical applications and non applications of those outcomes and explain the differences with previous results obtained in the limit of infinite search-space dimensionality.

Original languageEnglish
Title of host publicationParallel Problem Solving from Nature - PPSN X - 10th International Conference, Proceedings
EditorsGünter Rudolph, Thomas Jansen, Nicola Beume, Simon Lucas, Carlo Poloni
PublisherSpringer Verlag
Pages52-61
Number of pages10
ISBN (Electronic)9783540876991
ISBN (Print)3540876995, 9783540876991
DOIs
Publication statusPublished - 1 Jan 2008
Externally publishedYes
Event10th International Conference on Parallel Problem Solving from Nature, PPSN 2008 - Dortmund, Germany
Duration: 13 Sept 2008 → 17 Sept 2008

Publication series

NameLecture Notes in Computer Science
Volume5199
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on Parallel Problem Solving from Nature, PPSN 2008
Country/TerritoryGermany
CityDortmund
Period13/09/08 → 17/09/08

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