A New Continuous Action-Set Learning Automaton for Function Optimization

  • Hamid Beigy
  • M. R. Meybodi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2869)


In this paper, we study an adaptive random search method based on learning automaton for solving stochastic optimization problems in which only the noise-corrupted value of objective function at any chosen point in the parameter space is available. We first introduce a new continuous action-set learning automaton (CALA) and theoretically study its convergence properties, which implies the convergence to the optimal action. Then we give an algorithm, which needs only one function evaluation in each stage, for optimizing an unknown function.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Hamid Beigy
    • 1
  • M. R. Meybodi
    • 1
  1. 1.Soft Computing Laboratory, Computer Engineering DepartmentAmirkabir University of TechnologyTehranIran

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