Designing Neural Networks Using Genetic Algorithms with Graph Generation System
Hiroaki Kitano
Center for Machine Translation, Carnegie Mellon University,
Pittsburgh, PA 15213 USA
and
NEC Corporation, Tokyo, 108 Japan
Abstract
We present a new method of designing neural networks using the genetic algorithm. Recently there have been several reports claiming attempts to design neural networks using genetic algorithms were successful. However, these methods have a problem in scalability, i.e., the convergence characteristic degrades significantly as the size of the network increases. This is because these methods employ direct mapping of chromosomes into network connectivities. As an alternative approach, we propose a graph grammatical encoding that will encode graph generation grammar to the chromosome so that it generates more regular connectivity patterns with shorter chromosome length. Experimental results support that our new scheme provides magnitude of speedup in convergence of neural network design and exhibits desirable scaling property.
