Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4976566 | Journal of the Franklin Institute | 2006 | 21 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Hamid Beigy, M.R. Meybodi,