Apparently, deep neural networks have been making an impact recently. The layer-by-layer training of these networks has made it feasible to construct complex, deep, and well-performing neural networks. Still, I feel that some applications of deep learning models might benefit from global optimization approaches that do not easily get stuck in local optima. I haven't seen any research in this direction, though.
Couldn't evolutionary/biologically inspired algorithms (e.g., Particle Swarm Optimization, Differential Evolution) be used to make deep learning models more powerful? Or is the computing power necessary for this particular combination of techniques currently a limiting factor?