An evolutionary method for the design of generic neural networks

Authors:
D. Edwards;K. Brown;N. Taylor
Affiliations:
Dept. of Comput. & Electr. Eng., Heriot-Watt Univ., Edinburgh, UK;Dept. of Comput. & Electr. Eng., Heriot-Watt Univ., Edinburgh, UK;Dept. of Comput. & Electr. Eng., Heriot-Watt Univ., Edinburgh, UK
Venue:
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Year:
2002

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Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems

Applied Soft Computing

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Abstract

Hybrid systems using evolution to optimize neural network design or training are usually limited in scope and effectiveness. A system is presented that permits the widest variety of networks to be evolved using a two-stage GA approach. Networks generated for a benchmark machine learning task compare favourably with alternative methods.