Interval GA for evolving neural networks with interval weights and biases
Hidehiko Okada, Takashi Matsuse, Tetsuya Wada, Akira Yamashita · Society of Instrument and Control Engineers of Japan · 2012
In this paper, we propose an extension of genetic algorithm for neuroevolution of interval-valued neural networks. In the proposed GA, values in the genotypes are not real numbers but intervals. We apply our interval-valued GA (IvGA) to the approximate modeling of interval functions with interval-valued neural networks. Experimental results showed that INNs trained by our IvGA approximated a test function to a certain extent, despite the fact that the learning was not supervised.