Comparative evaluation of genetic algorithm and backpropagation for training neural networks
neural network training, backpropagation, epoch, genetic algorithms, global search algorithms, interpolation
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.
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.
DOI for the article
Management and Information Technology