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Proceedings Paper

Review of efforts combining neural networks and evolutionary computation
Author(s): David B. Fogel; Peter J. Angeline
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Paper Abstract

Since the widespread recognition of the capacity for neural networks to perform general function approximation, a variety of such mapping functions have been used to address difficult problems in pattern recognition, time series forecasting, automatic control, image compression, and other engineering applications. Although these efforts have met with considerable success, the design and training of neural networks have remained much of an art, relying on human expertise, trial, and error. More recently, methods in evolutionary computation, including genetic algorithms, evolution strategies, and evolutionary programming, have been used to assist in and automate the design and training of neural networks. This presentation offers a review of these efforts and discusses the potential benefits and limitations of such combinations.

Paper Details

Date Published: 6 April 1995
PDF: 4 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205162
Show Author Affiliations
David B. Fogel, Natural Selection, Inc. (United States)
Peter J. Angeline, Loral Federal Systems (United States)

Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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