Adaptive and intelligent systems (AIS) for learning, control and optimization in dynamic environments (Editorial)

An increasing number of real world applications, such as network traffic, network monitoring, power systems etc., generates non-stationary behaviors. Therefore, the model built in order to predict their dynamics, optimize their performance or control their behavior must be adapted over time. During the last decade, the modeling based on learning in non-stationary environments has received more and more attention in the machine learning and data mining communities. This is because the requirements from practical (industrial) side induced a permanent paradigm shift from classical batch off-line learning procedures (as conducted throughout the 80ties and 90ties to build up accurate and high-performance machine learning models) to incremental, adaptive algorithms which are able to evolve the model structures, architectures and parameters fully autonomously and on the fly, typical in a single-pass manner. It is especially the case within the scope of dynamic data streaming context, changing environmental conditions or as part of large-scale problems, e.g. web mining, multi-sensor networks, sequential video analysis, Big Data or predictive maintenance in factories for the future (FoF) as part of the Industry 4.0 activities (even an important standpoint of the Horizon 2020 objective programme, see https://ec.europa.eu/programmes/horizon2020/en/h2020-sections-projects). The necessity of such algorithms is also underlined by the increasing size of the databases and storages, which induces that conventional batch learning systems cannot be applied within a reasonable time frame and sufficient accuracy. Thus, a block-wise loading of the data has to be carried out, which again requires the usage of incremental and evolving learning algorithms.