Determining neural network connectivity using evolutionary programming

The application of evolutionary programming, a stochastic search technique, for determining connectivity in feedforward neural networks, is investigated. The method is capable of simultaneously evolving both the connection scheme and the network weights. The number of synapses is incorporated into an objective function so that network parameter optimization is done with respect to a connectivity cost as well as mean pattern error. Experimental results are shown using feedforward networks for simple binary mapping problems