  The topology of artificial neural networks has a significant effect on their
performance. Characterizing efficient topology is a field of promising research
in Artificial Intelligence. However, it is not a trivial task and it is mainly
experimented on through convolutional neural networks. We propose a hybrid
model which combines the tensor calculus of feed-forward neural networks with
Pseudo-Darwinian mechanisms. This allows for finding topologies that are well
adapted for elaboration of strategies, control problems or pattern recognition
tasks. In particular, the model can provide adapted topologies at early
evolutionary stages, and 'structural convergence', which can found applications
in robotics, big-data and artificial life.
