This session presents Anomaly Detection Neuroevolution (AD-NEv) – a multi-level optimized neuroevolution framework. The method adapts genetic algorithms for: i) creating an ensemble model based on the bagging technique; ii) optimizing the topology of single anomaly detection models; iii) non-gradient fine-tuning network parameters. The results prove that the models created by AD-NEv achieve significantly better results than the well-known anomaly detection deep learning models.