Journal of Global Optimization

, Volume 31, Issue 4, pp 601–612 | Cite as

Approximate Implementations of Pure Random Search in the Presence of Noise

  • David L. J. Alexander
  • David W. Bulger
  • James M. Calvin
  • H. Edwin. Romeijn
  • Ryan L. Sherriff


We discuss the noisy optimisation problem, in which function evaluations are subject to random noise. Adaptation of pure random search to noisy optimisation by repeated sampling is considered. We introduce and exploit an improving bias condition on noise-affected pure random search algorithms. Two such algorithms are considered; we show that one requires infinite expected work to proceed, while the other is practical.


Global optimisation Noisy objective function Pure random search Sequential analysis 


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Copyright information

© Springer 2005

Authors and Affiliations

  • David L. J. Alexander
    • 1
  • David W. Bulger
    • 2
  • James M. Calvin
    • 3
  • H. Edwin. Romeijn
    • 4
  • Ryan L. Sherriff
    • 5
  1. 1.College of SciencesMassey UniversityWellingtonNew Zealand
  2. 2.Department of StatisticsMacquarie UniversityAustralia
  3. 3.Department of Computer and Information ScienceNew Jersey Institute of TechnologyNew YorkU.S.A.
  4. 4.Department of Industrial and Systems EngineeringUniversity of FloridaGainesvilleU.S.A.
  5. 5.Institute of Information Sciences and TechnologyMassey UniversityPalmerston NorthNew Zealand

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