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GLOBAL OPTIMIZATION METHODS

Lonnie Hamm and B Brorsen

No 36631, 2002 Annual Meeting, July 28-31, 2002, Long Beach, California from Western Agricultural Economics Association

Abstract: Training a neural network is a difficult optimization problem because of numerous local minimums. Many global search algorithms have been used to train neural networks. However, local search algorithms are more efficient with computational resources, and therefore numerous random restarts with a local algorithm may be more effective than a global algorithm. This study uses Monte-Carlo simulations to determine the relative efficiency of a local search algorithm to 9 stochastic global algorithms. The computational requirements of the global algorithms are several times higher than the local algorithm and there is little gain in using the global algorithms to train neural networks.

Keywords: Research; Methods/; Statistical; Methods (search for similar items in EconPapers)
Date: 2002
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Persistent link: https://EconPapers.repec.org/RePEc:ags:waealb:36631

DOI: 10.22004/ag.econ.36631

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