Evolving Systems

期刊简介：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 applicationsbut 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 physics3) Applications in engineering, business, social sciences.

【译文】《演化的系统》涵盖动态演化的系统领域的调查、方法论和应用导向的论文。‘演化的系统’受到在动态变化和演化的环境中系统模型演化的想法的启发。与机器学习、数学建模和相关学科的标准方法相比，这些方法中模型结构是事先假设和固定的，问题集中在参数优化上，演化的系统允许模型结构逐渐变化/演化。这种持续或终身学习和领域适应的目标是自组织。它可以适应新的数据模式，更适合流数据、迁移学习和识别未知和不可预测的数据模式。这些特性对于在设计和运行时继续学习和适应的自主、机器人系统至关重要。该期刊征求解决非平稳、不可预测环境中系统建模、聚类、分类、预测和控制各方面问题的出版物，并描述它们的设计新方法和途径。该期刊致力于自发展、自组织和演化的系统主题——从系统方法到案例研究和实际工业应用。它涵盖了所有方法论方面，如演化的系统方法、演化的神经网络和神经模糊系统、演化的分类器和聚类、演化的控制器和预测模型、演化的可解释AI系统、演化的系统应用，同时也关注新的范例和应用，包括医学、机器人、商业、工业自动化、控制系统、交通、通信、环境监测、生物医学系统、安全和电子服务、金融和经济。所有提交的方法和系统的共同特征是系统的演化和环境。