Deep neural networks (DNN) are increasingly being used to perform
algorithm-selection in combinatorial optimisation domains, particularly as they
accommodate input representations which avoid designing and calculating
features. Mounting evidence from domains that use images as input shows that
deep convolutional networks are vulnerable to adversarial samples, in which a
small perturbation of an instance can cause the DNN to misclassify. However, it
remains unknown as to whether deep recurrent networks (DRN) which have recently
been shown promise as algorithm-selectors in the bin-packing domain are equally
vulnerable. We use an evolutionary algorithm (EA) to find perturbations of
instances from two existing benchmarks for online bin packing that cause
trained DRNs to misclassify: adversarial samples are successfully generated
from up to 56% of the original instances depending on the dataset. Analysis of
the new misclassified instances sheds light on the `fragility' of some training
instances, i.e. instances where it is trivial to find a small perturbation that
results in a misclassification and the factors that influence this. Finally,
the method generates a large number of new instances misclassified with a wide
variation in confidence, providing a rich new source of training data to create
more robust models.

深度神经网络逐渐被用于在组合优化领域中进行算法选择，尤其是在避免设计和计算特征的输入表示方面。然而，深度卷积网络在对抗示例方面存在漏洞，而最近在装箱问题领域中显示出潜力的深度递归网络是否同样易受攻击尚不清楚。本研究使用进化算法在在线装箱基准数据集中找到实例的扰动，导致训练的深度递归网络进行误分类。进一步的分析揭示了一些训练实例的脆弱性以及影响因素，并通过生成大量新的误分类实例提供了更丰富的训练数据以创建更强大的模型。