Stochastic Adaptive Search

  • Graham R. Wood
  • Zelda B. Zabinsky
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 62)


Large scale optimisation problems are often tackled using stochastic adaptive search algorithms, but the convergence of such methods to the global optimum is generally poorly understood. In recent years a variety of theoretical stochastic adaptive algorithms have been put forward and their favourable convergence properties confirmed analytically. Such research has two purposes: it offers some understanding of the convergence of stochastic adaptive methods while also providing motivation for the development of practical algorithms which approximate the ideal performance. This chapter summarises these developments.


Localisation Search Global Optimization Global Optimization Problem Termination Region Adaptive Search 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media Dordrecht 2002

Authors and Affiliations

  • Graham R. Wood
    • 1
  • Zelda B. Zabinsky
    • 2
  1. 1.Institute of Information Sciences and TechnologyMassey UniversityPalmerston NorthNew Zealand
  2. 2.Industrial EngineeringUniversity of WashingtonSeattleUSA

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