Evolving Systems (ES)

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). Evolving Systems solicits publications that address the problems of all aspects of system modelling, clustering, classification, prediction, anomaly detection and control in non-stationary, unpredictable environments and describe new methods and approaches for their design.

Evolving Systems (ES) is :-

• International, Peer-Reviewed, Open Access, Refereed, neuroscience, bioinformatics, and quantum physics. Welcomes applications in engineering, health, environment, security, transport business, social sciences, Control and Optimization, Computer Science Applications, Modelling and Simulation, Control and Systems Engineering , Online or Print , Bi-Monthly Journal

• UGC Approved, ISSN Approved: P-ISSN P-ISSN: 1868-6478, E-ISSN: 1868-6486, Established: 2010, Impact Factor: 2.7
• Does Not Provide Crossref DOI

• Not indexed in Scopus, WoS, DOAJ, PubMed, UGC CARE