Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index

This paper proposes genetic algorithms (GAs) approach to feature discretization and the determination of connection weights for artificial neural networks (ANNs) to predict the stock price index. Previous research proposed many hybrid models of ANN and GA for the method of training the network, feature subset selection, and topology optimization. In most of these studies, however, GA is only used to improve the learning algorithm itself. In this study, GA is employed not only to improve the learning algorithm, but also to reduce the complexity in feature space. GA optimizes simultaneously the connection weights between layers and the thresholds for feature discretization. The genetically evolved weights mitigate the well-known limitations of the gradient descent algorithm. In addition, globally searched feature discretization reduces the dimensionality of the feature space and eliminates irrelevant factors. Experimental results show that GA approach to the feature discretization model outperforms the other two conventional models. (C) 2000 Published by Elsevier Science Ltd.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
2000-08
Language
ENG
Keywords

GLOBAL OPTIMIZATION; BACKPROPAGATION; TRANSFORMATION; SELECTION; FUTURES

Citation

EXPERT SYSTEMS WITH APPLICATIONS, v.19, no.2, pp.125 - 132

ISSN
0957-4174
URI
http://hdl.handle.net/10203/3708
Appears in Collection
KGSF-Journal Papers(저널논문)
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