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Title

STOCHASTIC OPTIMIZATION USING CONTINUOUS ACTION-SET LEARNING AUTOMATA

Author(s)

BEYGY H. | MEYBODI M.R.

Pages

  14-25

Keywords

Not Registered.

Abstract

 In this paper, an adaptive random search method, based on continuous action-set learning automata, is studied for solving stochastic optimization problems in which only the noise-corrupted value of a function at any chosen point in the parameter space is available. First, a new continuous action-set learning automaton is introduced and its convergence properties are studied. Then, applications of this new continuous action-set learning automata to the minimization of a penalized Shubert function and pattern classification are presented.

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