Genetic generation of both the weights and architecture for a neural network

Shows how to find both the weights and architecture for a neural network, including the number of layers, the number of processing elements per layer, and the connectivity between processing elements. This is accomplished by using a recently developed extension to the genetic algorithm which genetically breeds a population of LISP symbolic expressions of varying size and shape until the desired performance by the network is successfully evolved. The novel `genetic programming´ paradigm is applied to the problem of generating a neural network for a one-bit adder