This approach originates from the question: how much does the architecture compared to the weights that affect the performance of a neuron network?

This article describes a non-traditional machine learning approach: using genetic algorithm to find NN architectures optimized for 1) weight-agnostic and 2) least complex. The resulting architecture works for a wide range of weight shared across the nodes.