  Filter pruning is a common method to achieve model compression and
acceleration in deep neural networks (DNNs).Some research regarded filter
pruning as a combinatorial optimization problem and thus used evolutionary
algorithms (EA) to prune filters of DNNs. However, it is difficult to find a
satisfactory compromise solution in a reasonable time due to the complexity of
solution space searching. To solve this problem, we first formulate a
multi-objective optimization problem based on a sub-network of the full model
and propose a Sub-network Multiobjective Evolutionary Algorithm (SMOEA) for
filter pruning. By progressively pruning the convolutional layers in groups,
SMOEA can obtain a lightweight pruned result with better
performance.Experiments on VGG-14 model for CIFAR-10 verify the effectiveness
of the proposed SMOEA. Specifically, the accuracy of the pruned model with
16.56% parameters decreases by 0.28% only, which is better than the widely used
popular filter pruning criteria.
