GLOBAL OPTIMIZATION METHODS
GLOBAL OPTIMIZATION METHODS
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发表时间：
2002
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通讯作者：
Lonnie Hamm;B. Brorsen
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文献类型：
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作者：
Lonnie Hamm;B. Brorsen
训练神经网络是一个困难的优化问题，因为有许多局部极小值。许多全局搜索算法已被用于训练神经网络。然而，局部搜索算法对于计算资源更有效，因此利用局部算法的多次随机重启可能比全局算法更有效。本研究使用蒙特-卡罗模拟，以确定相对效率的局部搜索算法的9个随机全局算法。全局算法的计算要求比局部算法高几倍，并且使用全局算法来训练神经网络几乎没有增益。
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.