Generative capacities of grammars codification for evolution of NN architectures

Abstract

Designing the optimal neural net (NN) architecture can be formulated as a search problem in the architectures space, where each point represents an architecture. The search space of all possible architectures is very large, and the task of finding the simplest architecture may be an arduous and mostly a random task. Methods based on indirect encoding have been used to reduce the chromosome length. In this paper, a new indirect encoding method is proposed and an analysis of the generative capacity of the method is presented.