Evolutionary Design of Arti cial Neural
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چکیده
|Evolutionary design of artiicial neural networks (ANNs) ooers a very promising and automatic alternative to designing ANNs manually. The advantage of evolutionary design over the manual design is their adaptability to a dynamic environment. Most research in evolving ANNs only deals with the topological structure of ANNs and little has been done on the evolution of both topological structures and node transfer functions. This paper presents a new automatic method to design general neural networks (GNNs) with diierent nodes. GNNs combine generalisa-tion capabilities of distributed neural networks (DNNs) and computational eeciency of local neural networks (LNNs). We use an evolutionary programming (EP) algorithm with new mutation operators which are very eeective for evolving GNN architectures and weights simultaneously. Our EP algorithm allows GNNs to grow as well as shrink during the evolutionary process. Our experiment results show the effectiveness and accuracy of evolved GNNs.
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تاریخ انتشار 1996