Recent advances on evolving intelligent systems and applications

During the last decade, evolving intelligent systems (EIS) have been become an important cornerstone for resolving any dynamic learning and modeling demands in (on-line) real-world modeling problems, especially with an intensified dynamic data streaming context, changing environmental conditions and system states 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. The necessity is underlined by the increasing dynamic and complexity of modeling problems as well as the increasing sizes of data bases and storages, which induce that conventional batch learning systems cannot be applied within a reasonable time frame and sufficient accuracy.