Neural networks are usually trained using gradient-based procedure but these methods find suboptimal solutions being trapped in local minima. Recently genetic algorithms have also been used for optimization of neural architectures. Other global optimization methods suitable for neural networks are competetive with genetic algorithms and are worth trying. A review: "Optimization and global minimization methods for neural networks" has been written but little implementations done so far.
Good topic for a PhD - still open !