Studying the Capacity of Grammatical Encoding to Generate FNN Architectures

Many methods to codify Artificial Neural Networks have been developed to avoid the defects of direct encoding schema, improving the search into the solution's space. A method to estimate how the search space is covered and how are the movements along search process applying genetic operators is needed in order to evaluate the different encoding strategies for Feedforward Neural Networks. A first step of this method is considered with two encoding strategies, a direct encoding method and an indirect encoding scheme based on graph grammars: generative capacity, how many different architectures the method is able to generate.