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.<
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