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)
Information Technology and Cybersecurity
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
Recommended Citation
Sexton, Randall S., and Jatinder ND Gupta. "Comparative evaluation of genetic algorithm and backpropagation for training neural networks." Information Sciences 129, no. 1-4 (2000): 45-59.
Journal Title
Information Sciences
