Variable encoding of modular neural networks for time series prediction

The combination of evolutionary algorithms and neural networks for the purpose of structure optimization has frequently been discussed. In this paper we apply an indirect encoding method, the recursive encoding combined with a gradual growth process of the network structure, to the problem of time series prediction and modelling. Modularity of the network structure, the optimization of the encoding parameters on a larger time-scale, i.e., a meta-evolutionary process and the choice of encoding dependent search operators to enhance the strong causality of the search process are discussed