In this paper we explore methods of enhancing the evolvability of a particular device. We assume that the device may be specified by a table of inputs and outputs. We investigate a method of extracting the topological structure of the device from rarified absolute Hessian matrices (raH matrices) and using this topological information as the basis for construction of solutions to evolutionary problems. We validate the algorithm by demonstrating its ability to extract the structure of devices to be evolved from the input/output table. Moreover, we validate this structure by using a genetic algorithm to train a perceptron, yielding perceptrons which solve the computational problem with error rates of less than 4%.