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.

《进化系统》涵盖了动态进化系统领域的调查、方法论和应用导向论文。‘进化系统’的灵感来自于动态变化和进化环境中系统模型进化的理念。与机器学习、数学建模和相关学科中的标准方法不同，这些方法假设并先验地固定模型结构，问题集中在参数优化上，而进化系统允许模型结构逐渐改变/进化。这种持续或终身学习和领域适应的目的是自我组织。它可以适应新的数据模式，更适合流数据、迁移学习，并且可以识别和学习未知和不可预测的数据模式。这些特性对于自主机器人系统至关重要，因为这些系统在设计完成后（运行时）会继续学习和适应。

《Evolving Systems》征集的出版物旨在解决非平稳、不可预测环境中系统建模、聚类、分类、预测和控制的各个方面的问题，并描述其设计的新方法和途径。

该期刊致力于从系统方法到案例研究和实际工业应用的自我开发、自我组织和进化系统的主题。它涵盖了方法论的各个方面，例如

不断发展的系统方法

不断发展的神经网络和神经模糊系统

不断发展的分类器和聚类

不断发展的控制器和预测模型

不断发展的可解释人工智能系统

不断发展的系统应用

而且还关注新的范式和应用，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和环境的不断发展。

该期刊涵盖与以下内容相关的贡献：

1）机器学习、人工智能、计算智能和数学建模方法

2）来自自然和生物学的灵感，包括神经科学、生物信息学和分子生物学、量子物理学

3）在工程、商业、社会科学中的应用。

该刊已被国际权威数据库SCIE收录，该刊致力于发表经过严格同行评审的高质量原创文章，反映COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE领域的新进展、新技术、新成果，促进该领域科研交流和科研成果转化。该刊2023年影响因子为2.7，平均审稿速度为 ，近四年来没有被列入预警名单。如果您需要投稿发表服务及指导，可以联系我们的客服老师，我们专业专注服务期刊投稿协助10年，为您提供期刊投稿个性化定制服务，并且我们确保严格保密您的个人信息及稿件内容。