A Model for the Dynamic Interaction Between Evolution and Learning

The interaction between learning and evolution has elicited much interest particularly among researchers who use evolutionary algorithms for the optimization of neural structures. In this article, we will propose an extension of the existing models by including a developmental phase – a growth process – of the neural network. In this way, we are able to examine the dynamical interaction between genetic information and information learned during development. Several measures are proposed to quantitatively examine the benefits and the effects of such an overlap between learning and evolution. The proposed model, which is based on the recursive encoding method for structure optimization of neural networks, is applied to the problem domain of time series prediction. Furthermore, comments are made on problem domains which associate growing networks (size) during development with problems of increasing complexity.