Evolving Multiple Neural Networks
In this paper, we apply genetic algorithms to the automatic generation of neural networks as well as the biological inspiration of neural networks to successfully adapt to environments. The network produced by this method can be customized for a special objective because the network is selected by the objective function. The final goal in designing a classifier is to achieve the best performance for a given classification. It has been observed that some methods of combining networks consistently outperform a single network. Therefore, we also investigate the performance of combining multiple evolving neural networks. Financial and medical data are used to test the network's performance.
S. Sohn and C. H. Dagli, "Evolving Multiple Neural Networks," ANNIE 2002, American Society of Mechanical Engineers (ASME), Jan 2002.
Engineering Management and Systems Engineering
Keywords and Phrases
Artificial Intelligence; Neural Networks
Article - Conference proceedings
© 2002 American Society of Mechanical Engineers (ASME), All rights reserved.
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