Title

Comparative evaluation of genetic algorithm and backpropagation for training neural networks

Abstract

In view of several limitations of gradient search techniques (e.g. backpropagation), global search techniques, including evolutionary programming and genetic algorithms (GAs), have been proposed for training neural networks (NNs). However, the effectiveness, ease-of-use, and efficiency of these global search techniques have not been compared extensively with gradient search techniques. Using five chaotic time series functions, this paper empirically compares a genetic algorithm with backpropagation for training NNs. The chaotic series are interesting because of their similarity to economic and financial series found in financial markets.

Department(s)

Management and Information Technology

Document Type

Article

DOI

https://doi.org/10.1016/s0020-0255(00)00068-2

Keywords

neural network training, backpropagation, epoch, genetic algorithms, global search algorithms, interpolation

Publication Date

2000

Journal Title

Information Sciences

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