the noise associated with conventional techniques for evolutionary improvement of neural network architectures is reduced so that of an optimum architecture can be determined more efficiently and more effectively . parameters that affect the initialization of a neural network architecture are included within the encoding that is used by an evolutionary algorithm to optimize the neural network architecture . the example initialization parameters include an encoding that determines the initial nodal weights used in each architecture at the commencement of the training cycle . by including the initialization parameters within the encoding used by the evolutionary algorithm , the initialization parameters that have a positive effect on the performance of the resultant evolved network architecture are propagated and potentially improved from generation to generation . conversely , initialization parameters that , for example , cause the resultant evolved network to be poorly trained , will not be propagated . in accordance with a second aspect of this invention , the encoding also includes parameters that affect the training process , such as the duration of the training cycle , the training inputs applied , and so on . in accordance with a third aspect of this invention , the same set of training or evaluation inputs are applied to all members whose performances are directly compared .