<![CDATA[Deploying deep convolutional neural networks (CNNs) on edge devices requires balancing model accuracy and computational efficiency. While structured pruning limits inference costs by removing redundant filters, most methods apply a rigid, global criterion, ignoring the distinct representational roles of individual layers. This yields suboptimal results, especially under aggressive compression where over-pruning degrades performance. To address this limitation, we propose an adaptive structured pruning framework based on genetic algorithms (GAs) that jointly optimizes layer-wise pruning ratios and strategies. Each layer independently selects between min-importance and median-rank pruning, enabling the exploration of tailored strategy combinations. A training-free taylor based proxy fitness function ensures efficient candidate evaluation without re peated fine-tuning. After fine-tuning the selected architecture, experiments on VGG16 demonstrate that our method achieves 92.78 ± 0.28% accuracy (over 50 independent runs) with a 70.0 ± 3.2% MACsreductiononCIFAR-10, andmaintains 71.82% accuracy on CIFAR-100. These results demonstrate competitive performance compared to existing pruning methods while achieving substantial computational cost reduction.]]>
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