Comparative performance of backpropagation networks designed by genetic algorithms and heuristics

Author: Hansen J.V.^{1, *}

Source: International Journal of Intelligent Systems in Accounting, Finance & Management, Volume 7, Number 2, June 1998 , pp. 69-79(11)

Publisher: John Wiley & Sons, Ltd.

Buy & download fulltext article:

The full text article is not available for purchase.

The publisher only permits individual articles to be downloaded by subscribers.

Abstract:

The design of neural network models involves numerous complexities, including the determination of input vectors, choosing the number of hidden layers and their computational units, and specifying activation functions for the latter. The combinatoric possibilities are daunting, yet experience has yielded informal guidelines that can be useful. Alternatively, current research on genetic algorithms (GA) suggests that they might be of practical use as a formal method of determining ‘good’ architectures for neural networks. In this paper, we use a genetic algorithm to find effective architectures for backpropagation neural networks (BP). We compare the performance of heuristically designed BP networks with that of GA-designed BP networks. Our test domains are sets of problems having compensatory, conjunctive, and mixed-decision structures. The results of our experiment suggest that heuristic methods produce architectures that are simpler and yet perform comparatively well. © 1998 John Wiley & Sons, Ltd.

Language: English

Document Type: Research article

DOI: http://dx.doi.org/10.1002/(SICI)1099-1174(199806)7:2<69::AID-ISAF143>3.0.CO;2-2

Affiliations: 1: Marriott School of Management, Brigham Young University, USA *

Publication date: 1998-06-01

More about this publication?

As of January 2010, this journal will no longer be available on IngentaConnect, please visit Wiley InterScience to arrange continued access to this content
Access this journal at Wiley InterScience
ingentaconnect is not responsible for the content or availability of external websites