Adaptive evolutional learning method of neural networks using genetic algorithms under dynamic environments

Backpropagation learning and genetic algorithms are widely known for their superior adaptation capability by imitating mechanisms of a living thing. However, most studies in this field have been developed under static environments. Once input-output patterns change, the trained network under static environments should start training from the initial state. On the contrary, if their algorithms have a sufficient adaptive ability under dynamic environments, they can work like a living thing´s evolutionary process. We propose an adaptive evolutional learning method of neural networks using genetic algorithms, which can perform effective learning under dynamic environments