Designing neural networks by adaptively building blocks in cascades

Abstract

We present a novel approach for the structuring of artificial neural networks by an Evolutionary Algorithm. The structuring problem is discussed as an example of a pseudo-boolean optimization problem. The continuous Evolution Strategy is extended by an individual developmental process, in our case a stochastic generation scheme, to allow the use of adaptive genetic operators. On exemplary tests we analyze the performance and evaluate the efficiency of this approach.