Evolving Systems covers surveys, methodological, and application-oriented papers in the area of dynamically evolving systems addressing continual (life-long) learning, open-set classification, self-learning and self-developing, self-evolving models and systems. Evolving systems are inspired by the idea of system model evolution in a dynamically changing and evolving environment. In contrast to the standard approach in machine learning, mathematical modelling, and related disciplines where the model structure is assumed and fixed a priori and the problem is primarily focused on parametric optimisation, evolving systems allow the learning representations and the model structure or architecture to gradually change/evolve. The aim of such continuous or life-long learning and domain adaptation is self-organisation. It can adapt to new data patterns, is more suitable for streaming data, transfer learning and can recognise and learn from unknown and unpredictable data patterns. Such properties are critically important for autonomous, robotic systems that continue learning and adapting after being designed (at run time).