Evolutionary optimised ontogenetic neural networks with incremental problem complexity during development

In order to optimise unconstrained, large neural network structures with evolutionary algorithms, indirect encodings have been proposed. However, if the evolutionary process is combined with network learning, which is sensible both with respect to technical applications in dynamical environments and to the biological paragon, a way has to be found to combine learning with the evolutionary optimisation of such large structures. Utilising the development of neural systems during ontogeny seems a logical starting point for the realization of a step by step learning in networks. Furthermore, the combination of network growth during the developmental phase with an incremental problem complexity might allow the optimisation of large network structures together with learning. The author proposes a model to simulate such a combined approach and applies it to the problem of time series modelling. By introducing several measures for the transfer of information from one developmental step to the next, we will be able to quantitatively analyse the behaviour of the proposed model