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A new continuous action-set learning automaton for function optimization
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DOI：10.1016/j.jfranklin.2005.07.004.png)
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Abstract
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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. (c) 2005 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
Keywords:
CAPACITY ASSIGNMENT PROBLEM
STOCHASTIC OPTIMIZATION
REINFORCEMENT SCHEMES
NETWORKS
ALGORITHMS
DESIGN
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