  Multivariate time series anomaly detection is a very common problem in the
field of failure prevention. Fast prevention means lower repair costs and
losses. The amount of sensors in novel industry systems makes the anomaly
detection process quite difficult for humans. Algorithms which automates the
process of detecting anomalies are crucial in modern failure-prevention
systems. Therefore, many machine and deep learning models have been designed to
address this problem. Mostly, they are autoencoder-based architectures with
some generative adversarial elements. In this work, a framework is shown which
incorporates neuroevolution methods to boost the anomaly-detection scores of
new and already known models. The presented approach adapts evolution
strategies for evolving ensemble model, in which every single model works on a
subgroup of data sensors. The next goal of neuroevolution is to optimise
architecture and hyperparameters like window size, the number of layers, layer
depths, etc. The proposed framework shows that it is possible to boost most of
the anomaly detection deep learning models in a reasonable time and a fully
automated mode. The tests were run on SWAT and WADI datasets. To our knowledge,
this is the first approach in which an ensemble deep learning anomaly detection
model is built in a fully automatic way using a neuroevolution strategy.
