On the Parallel Speed-Up of Estimation of Multivariate Normal Algorithm and Evolution Strategies

  • Authors:
  • Fabien Teytaud;Olivier Teytaud

  • Affiliations:
  • TAO (Inria), LRI, UMR 8623(CNRS - Univ. Paris-Sud), bat 490 Univ. Paris-Sud, Orsay, France 91405;TAO (Inria), LRI, UMR 8623(CNRS - Univ. Paris-Sud), bat 490 Univ. Paris-Sud, Orsay, France 91405

  • Venue:
  • EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
  • Year:
  • 2009

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Abstract

Motivated by parallel optimization, we experiment EDA-like adaptation-rules in the case of *** large. The rule we use, essentially based on estimation of multivariate normal algorithm, is (i) compliant with all families of distributions for which a density estimation algorithm exists (ii) simple (iii) parameter-free (iv) better than current rules in this framework of *** large. The speed-up as a function of *** is consistent with theoretical bounds.