Automatic generation of a neural network architecture using evolutionary computation

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 architecture generation. In this paper a brief introduction to the field is given as well as an implementation of automatic neural network generation using genetic programming.<>