AD-NEv++：基于多架构神经进化的多元异常检测框架
AD-NEv++ : The multi-architecture neuroevolution-based multivariate anomaly detection framework
AI总结：
针对现有神经进化异常检测框架支持的网络层类型有限、模型优化繁琐的问题，提出三阶段框架AD-NEv++，结合子空间进化、模型进化与微调，支持多种网络层，在基准测试中性能优于现有深度学习及神经进化方法，超越最优GNN架构。
摘要：
异常检测工具与方法为现代信息物理系统及基于传感器的系统提供了关键的分析能力。尽管用于异常检测的深度学习架构发展迅速，但针对特定数据集的模型优化仍是一项繁琐且耗时的工作。神经进化作为一种搜索最优神经网络的全自动化方法，同时支持梯度与非梯度微调，有望成为解决这一问题的高效方案。然而，现有的融合神经进化的框架缺乏对新型层与架构的支持，通常仅局限于卷积层与LSTM层。本文提出AD-NEv++，这是一种三阶段的基于神经进化的方法，将子空间进化、模型进化与微调协同结合。我们的方法通过优化神经进化过程中的变异算子，同时支持包括注意力层、全连接层与图卷积层在内的多种神经层，克服了现有方法的局限性。我们采用广泛使用的多元异常检测基准数据集开展了大量实验评估，结果表明AD-NEv++生成的模型性能优于知名的深度学习架构以及基于神经进化的异常检测方法。此外，结果显示AD-NEv++在所有异常检测基准上均能改进并超越当前最优的GNN（图神经网络）模型架构。
英文摘要：
Anomaly detection tools and methods enable key analytical capabilities in modern cyberphysical and sensor-based systems. Despite the fast-paced development in deep learning architectures for anomaly detection, model optimization for a given dataset is a cumbersome and time-consuming process. Neuroevolution could be an effective and efficient solution to this problem, as a fully automated search method for learning optimal neural networks, supporting both gradient and non-gradient fine tuning. However, existing frameworks incorporating neuroevolution lack of support for new layers and architectures and are typically limited to convolutional and LSTM layers. In this paper we propose AD-NEv++, a three-stage neuroevolution-based method that synergically combines subspace evolution, model evolution, and fine-tuning. Our method overcomes the limitations of existing approaches by optimizing the mutation operator in the neuroevolution process, while supporting a wide spectrum of neural layers, including attention, dense, and graph convolutional layers. Our extensive experimental evaluation was conducted with widely adopted multivariate anomaly detection benchmark datasets, and showed that the models generated by AD-NEv++ outperform well-known deep learning architectures and neuroevolution-based approaches for anomaly detection. Moreover, results show that AD-NEv++ can improve and outperform the state-of-the-art GNN (Graph Neural Networks) model architecture in all anomaly detection benchmarks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
