Lamarckian neuroevolution for visual control in the quake II environment
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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Learning plays an important role in neural computing, but it takes long time when the input data set is large and complex. Many papers have proposed how to implement learning algorithms on parallel machines or a cluster of computers to reduce learning time in the past. In this article, we present a distributed backpropagation learning that distributes the data set to learn in a cluster of computers. Our experiment results reveal that the error calculated by it is closer with the convention pattern mode backpropagation learning, and the time used by it is faster when the data is complex. Due to that the development and maintenance of distributed applications using conventional techniques are time-consuming, and that the applications may not be extensible, we use the CORBA technique as our implementation middleware. Thus, we can efficiently implement our distributed backpropagation learning on a cluster of computers