Implementation of Stochastic Adaptive Search with Hit-and-Run as a Generator

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


The Hit-and-Run algorithm iteratively generates a sequence of points in a set by taking steps of random length in randomly generated directions. Hit-and-Run is used as a sampling technique within a global optimization algorithm, as a generator in the context of simulated annealing. The ultimate goal is to approximate the desirable properties of stochastic adaptive search, in particular, the linear complexity of pure adaptive search. Such an algorithm is the Hit-and-Run version of simulated annealing with temperature equal to zero, called Improving Hit-and-Run because only points improving the objective function are accepted. The Hit-and-Run generator coupled with a Metropolis acceptance criterion is called Hide-and-Seek, and is motivated by theoretical results from adaptive search. Other variations of Hit-and-Run are also described here, as applied to global optimization.


Simulated Annealing Global Optimization Feasible Region Coordinate Direction Global Optimization Problem 
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

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

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