Automatic Generation of Neural Network Architecture Using Evolutionary Computation

نویسندگان

  • E. Vonk
  • Lakhmi C. Jain
  • R. P. Johnson
چکیده

This paper reports the application of evolutionary computation in the automatic generation of a neural network architecture. It is a usual practice to use trial and error to find a suitable neural network architecture. This is not only time consuming but may not generate an optimal solution for a given problem. The use of evolutionary computation is a step towards automation in neural network architecture generation. In this paper a brief introducuon to the field is given as well as an implementation of automatic neural network generation using genetic programming.

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تاریخ انتشار 1995