AD-NEv++: A Scalable Multi-Architecture Neuroevolution Framework for Multivariate Anomaly Detection

AD-NEv++ is a neuroevolution-based framework that synergically combines subspace evolution, model evolution, and fine-tuning to optimize autoencoder architectures, including graph-based models, for multivariate anomaly detection. The framework supports a wide spectrum of neural layers, including attention, dense, and graph convolutional layers, and outperforms well-known deep learning architectures and neuroevolution-based approaches on benchmark datasets.