神经控制器优化的混合遗传方法

CompEuro 1992 Proceedings Computer Systems and Software Engineering Pub Date : 1992-05-04 DOI:10.1109/CMPEUR.1992.218440

J. Heistermann

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引用次数: 10

摘要

作者讨论了遗传算法(GAs)的一些功能。将GAs与其他标准优化方法(如梯度下降法或模拟退火法)进行了比较。证明了SA只是GA的一个特例。通过实例说明了种群在优化过程中的作用。将遗传算法作为一种学习算法应用于神经网络。

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A mixed genetic approach to the optimization of neural controllers

The author discusses some of the capabilities of genetic algorithms (GAs). GAs are compared with other standard optimization methods like gradient descent or simulated annealing (SA). It is shown that SA is just a special case of GA. The role of a population in the optimization process is demonstrated by an example. GA was applied as a learning algorithm to neural networks.<>

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CompEuro 1992 Proceedings Computer Systems and Software Engineering

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