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

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5 Author(s)
Oeda, S. ; Fac. of Inf. Sci., Hiroshima Univ., Japan ; Ichimura, T. ; Terauchi, M. ; Takahama, T.
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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

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Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 2000. Proceedings. Fourth International Conference on  (Volume:2 )

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