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A new continuous action-set learning automaton for function optimization

Beigy, H ; Sharif University of Technology | 2006

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  1. Type of Document: Article
  2. DOI: 10.1016/j.jfranklin.2005.07.004
  3. Publisher: 2006
  4. Abstract:
  5. In this paper, we study an adaptive random search method based on continuous action-set learning automaton for solving stochastic optimization problems in which only the noise-corrupted value of function at any chosen point in the parameter space is available. We first introduce a new continuous action-set learning automaton (CALA) and study its convergence properties. Then we give an algorithm for optimizing an unknown function. © 2005 The Franklin Institute. Published by Elsevier Ltd. All rights reserved
  6. Keywords:
  7. Optimization ; Parameter estimation ; Problem solving ; Random processes ; Search engines ; Set theory ; Optimization problem ; Parameter space ; Random search ; Automation
  8. Source: Journal of the Franklin Institute ; Volume 343, Issue 1 , 2006 , Pages 27-47 ; 00160032 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/pii/S0016003205000645