Evolving Systems covers surveys| methodological| and application-oriented papers in the area of dynamically evolving 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 focused on parametric optimisation| evolving systems allow the model structure to gradually change/evolve. The aim of such continuous or life-long learning and domain adaptation is self-organization. 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 to learn and adapt after they are being designed (at run time). Evolving Systems solicits publications that address the problems of all aspects of system modelling| clustering| classification| prediction and control in non-stationary| unpredictable environments and describe new methods and approaches for their design. The journal is devoted to the topic of self-developing| self-organised| and evolving systems in its entirety — from systematic methods to case studies and real industrial applications. It covers all aspects of the methodology such as Evolving Systems methodology Evolving Neural Networks and Neuro-fuzzy Systems Evolving Classifiers and Clustering Evolving Controllers and Predictive models Evolving Explainable AI systems Evolving Systems applications but also looking at new paradigms and applications| including medicine| robotics| business| industrial automation| control systems| transportation| communications| environmental monitoring| biomedical systems| security| and electronic services| finance and economics. The common features for all submitted methods and systems are the evolving nature of the systems and the environments. The journal is encompassing contributions related to: 1) Methods of machine learning| AI| computational intelligence and mathematical modelling 2) Inspiration from Nature and Biology| including Neuroscience| Bioinformatics and Molecular biology| Quantum physics 3) Applications in engineering| business| social sciences.
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