  The brain, as the source of inspiration for Artificial Neural Networks (ANN),
is based on a sparse structure. This sparse structure helps the brain to
consume less energy, learn easier and generalize patterns better than any other
ANN. In this paper, two evolutionary methods for adopting sparsity to ANNs are
proposed. In the proposed methods, the sparse structure of a network as well as
the values of its parameters are trained and updated during the learning
process. The simulation results show that these two methods have better
accuracy and faster convergence while they need fewer training samples compared
to their sparse and non-sparse counterparts. Furthermore, the proposed methods
significantly improve the generalization power and reduce the number of
parameters. For example, the sparsification of the ResNet47 network by
exploiting our proposed methods for the image classification of ImageNet
dataset uses 40 % fewer parameters while the top-1 accuracy of the model
improves by 12% and 5% compared to the dense network and their sparse
counterpart, respectively. As another example, the proposed methods for the
CIFAR10 dataset converge to their final structure 7 times faster than its
sparse counterpart, while the final accuracy increases by 6%.
