Analysis for characteristics of GA-Based learning method of binary neural networks

Authors:
Tatsuya Hirane;Tetsuya Toryu;Hidehiro Nakano;Arata Miyauchi
Affiliations:
Department of Computer Science and Media Engineering, Musashi Institute of Technology, Tokyo, Japan;Department of Computer Science and Media Engineering, Musashi Institute of Technology, Tokyo, Japan;Department of Computer Science and Media Engineering, Musashi Institute of Technology, Tokyo, Japan;Department of Computer Science and Media Engineering, Musashi Institute of Technology, Tokyo, Japan
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Year:
2005

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Abstract

In this paper, we analyze characteristics of GA-based learning method of Binary Neural Networks (BNN). First, we consider coding methods to a chromosome in a GA and discuss the necessary chromosome length for a learning of BNN. Then, we compare some selection methods in a GA. We show that the learning results can be obtained in the less number of generations by properly setting selection methods and parameters in a GA. We also show that the quality of the learning results can be almost the same as that of the conventional method. These results can be verified by numerical experiments.