1998 Volume 118 Issue 7-8 Pages 1114-1121
For the purpose of automatic and effective neural network structure design, a designing method is proposed based on Genetic Algorithms (GA). Neural Network structures are encoded on chromosomes in such a way that a number of different but related network structures result by changing a parameter in the decoding process. The GA finds an optimal chromosome that provides good network structures for a family of data sets. When a change takes place in the environment around the neural network, by changing the parameter, we can obtain a new neural network suitable for the new environment without re-running the GA again.
The proposed method is described by using, as an example, a problem of finding good neural network structures for data sets with different noise magnitudes. To obtain an accurate but non-overfitted neural network for the noisy data set, we introduce a relevant fitness function, a method for noise magnitude estimation, and a systematic way to determine the control parameter value of the decoding process. By incorporating these techniques in the network structure designing method mentioned above, we obtain a neural network which has a good generalization ability for each of data sets with different noise magnitudes.
The transactions of the Institute of Electrical Engineers of Japan.C
The Journal of the Institute of Electrical Engineers of Japan