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.

翻译：人工神经网络的表层学对其性能有重大影响。 定性高效的表层学是人造智能领域很有希望的研究领域。 但是,这不是一个微不足道的任务,而是主要通过进化神经网络进行实验。 我们提议了一个混合模型,将进化神经网络的高分微微微分与普塞多-达尔文机制结合起来。 这样可以找到适合制定战略、控制问题或模式识别任务的表层学。 特别是, 该模型可以在早期进化阶段提供经调整的表层学, 以及“ 结构趋同”, 可以在机器人、大数据和人工生命中找到应用。