# Neural Network Pruning by Cooperative Coevolution Source: [Neural Network Pruning by Cooperative Coevolution]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:11:46.889Z Authors: Haopu Shang, Jia-Liang Wu, Wenjing Hong, Chao Qian Published: 2022-04-12T09:06:38.000Z ## Abstract Neural network pruning is a popular model compression method which can significantly reduce the computing cost with negligible loss of accuracy. Recently, filters are often pruned directly by designing proper criteria or using auxiliary modules to measure their importance, which, however, requires expertise and trial-and-error. Due to the advantage of automation, pruning by evolutionary algorithms (EAs) has attracted much attention, but the performance is limited for deep neural networks as the search space can be quite large. In this paper, we propose a new filter pruning algorithm CCEP by cooperative coevolution, which prunes the filters in each layer by EAs separately. That is, CCEP reduces the pruning space by a divide-and-conquer strategy. The experiments show that CCEP can achieve a competitive performance with the state-of-the-art pruning methods, e.g., prune ResNet56 for $63.42\\%$ FLOPs on CIFAR10 with $-0.24\\%$ accuracy drop, and ResNet50 for $44.56\\%$ FLOPs on ImageNet with $0.07\\%$ accuracy drop. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()