Studying the capacity of cellular encoding to generate feedforward neural network topologies

Abstract

Many methods to codify artificial neural networks have been developed to avoid the disadvantages of direct encoding schema, improving the search into the solution's space. A method to analyse 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 multilayer perceptrons (MLP). In this paper, the generative capacity, this is how the search space is covered for a indirect scheme based on cellular systems, is studied. The capacity of the methods to cover the search space (topologies of MLP space) is compared with the direct encoding scheme.