  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.
