Forward-Backward Building Blocks for Evolving Neural Networks With Intrinsic Learning Behaviours

. This paper describes the forward-backward module: a simple building block that allows the evolution of neural networks with intrinsic supervised learning ability. This expands the range of networks that can be efficiently evolved compared to previous approaches, and also enables the networks to be invertible i.e. once a network has been evolved for a given problem domain, and trained on a particular dataset, the network can then be run backwards to observe what kind of mapping has been learned, or for use in control problems. A demonstration is given of the kind of self-training networks that could be evolved. 1 Introduction Despite much research into evolving neural networks, there is relatively little work published on evolving learning behaviours for neural networks. Within this area we can identify four kinds of approach: -- Write down a set of learning rules with variable parameters for a given network architecture, then evolve the choice of rules and their parameters. -- Evo..