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Yasumasa IKUNO Hiroaki HAWABATA Yoshiaki SHIRAO Masaya HIRATA Toshikuni NAGAHARA Yashio INAGAKI
Recently, the back propagation method, which is one of the algorithms for learning neural networks, has been widely applied to various fields because of its excellent characteristics. But it has drawbacks, for example, slowness of learning speed, the possibility of falling into a local minimum and the necessity of adjusting a learning constant in every application. In this article we propose an algorithm which overcomes some of the drawbacks of the back propagation by using an improved genetic algorithm.