An adaptive evolutional neuro learning method using genetic search and extraction of rules from trained networks

BP learning is widely known to perform good classification for given training data. However, there is a kind of noise or inconsistent knowledge in training cases. In this case, a neural network will not converge. To avoid such a problem, we propose an adaptive evolutional neuro learning method to handle a subset of the complete set of training cases. This method has a sufficient adaptive ability like a living thing´s evolutionary process based on Darwinian Genetic Inheritance. In this method, the network structure is determined by genetic search for each generation and the connection weights and learning parameters determined by BP learning are not inherited. Furthermore, we tried to extract rules from the trained network. To verify the validity and effectiveness of the proposed method, we develop the diagnostic system for hepatobiliary disorders