Continuous action set learning automata for stochastic optimization
type: Item
instance of: scholarly article
title: Continuous action set learning automata for stochastic optimization
zbMATH Open document ID: 0838.93083
DOI: 10.1016/0016-0032(94)90039-6
author: G. Santharam
author: P. S. Sastry
author: M. A. L. Thathachar
published in: Journal of the Franklin Institute
publication date: 1996-05-30T00:00:00Z
review text: The paper deals with the minimization of a function whose values cannot be determined exactly. However, it is assumed that it is possible (at any chosen point) to observe a random element with the mathematical expectation equal to the corresponding function value. Such situation appears for example in pattern recognition, signal processing, etc. It is well-known, from the mathematical optimization theory, that the stochastic approximation method can be employed to solve the above-mentioned problem. The other possibility is to employ a random search procedure. This investigation deals with an adaptive search procedure that generalizes the traditional model of a learning automaton. The Gaussian distribution is employed. This type of distribution is determined only by two parameters in the one-dimensional case, and by a finite number of parameters in general case. The one-dimensional case and multidimensional case are investigated separately. The convergence properties of the algorithm are investigated theoretically. Simulation results are presented at the end.
Mathematics Subject Classification ID: 93E35
Mathematics Subject Classification ID: 68Q45
zbMATH DE Number: 799598
zbMATH Keywords: adaptive search
zbMATH Keywords: learning automaton
zbMATH Keywords: Gaussian distribution
reviewed by: Vlasta Kaňková
MaRDI profile type: Publication
full work available at URL: 0016-0032(94)90039-6
OpenAlex ID: W2076847403
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description: scientific article; zbMATH DE number 799598