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Evolving Systems
期刊名:

Evolving Systems

期刊名缩写:EVOL SYST-GER (此期刊被最新的JCR期刊SCIE收录)

期刊收录信息: SCIE Scopus收录

信息更新时间:2023年12月
  • 影响因子: 3.2
  • 出版国家或地区: GERMANY
  • 期刊ISSN: 1868-6478
  • 出版商: SPRINGER HEIDELBERG
  • E-ISSN: 1868-6486
  • 出版周期: 6 issues per year
  • JCR分区: Q3
  • 出版语言: English
  • 自引率: 6.20%
  • 出版年份: 0
  • 是否OA开放访问: NO
  • 期刊官方网站: https://www.springer.com/12530
  • 年文章数: 74
  • 期刊投稿网址: https://www.editorialmanager.com/evos/
  • Gold OA文章占比: 3.56%
  • 通讯方式: TIERGARTENSTRASSE 17, HEIDELBERG, GERMANY, D-69121
  • 期刊导读: 《Evolving Systems》杂志,2023年发布的影响因子为:3.2 ,中科院分区:4区,JCR分区:Q3,该期刊是由 GERMANY, SPRINGER HEIDELBERG 出版的计算机科学类学术期刊,主要刊载计算机科学相关领域的原创研究文章和评论文章,该期刊目前收录在 【SCIE】 【Scopus收录】 等数据库,平均审稿速度(),平均录用比例() 123学术网专业SCI论文编辑服务(包括SCI论文英语润色,同行资深专家修改润色,SCI论文专业翻译,SCI论文格式排版,专业学术制图,发表等)帮助作者准备稿件,如自行投稿请联系《Evolving Systems》杂志官方:https://www.springer.com/12530,《Evolving Systems》通讯地址为:TIERGARTENSTRASSE 17, HEIDELBERG, GERMANY, D-69121。详细的期刊简介下拉到底部查看!
《Evolving Systems》JCR分区:Q3
按学科分区 JIF分区 JIF排名 JIF百分位

学科:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

分类:SCIE

Q3 83/145
43.1%
中科院《国际期刊预警名单(试行)》名单
2023年01月发布的2023版:不在预警名单中

2021年12月发布的2021版:不在预警名单中

2021年01月发布的2020版:不在预警名单中

《国际期刊预警名单(试行)》2023版共计包含28本期刊(查看


《国际期刊预警名单(试行)》2021版共计包含35本期刊(查看


《国际期刊预警名单(试行)》2020版共计包含65本期刊(查看

《Evolving Systems》中科院SCI期刊分区

2023年12月最新升级版:

大类学科 小类学科 Top 综述期刊
计算机科学 4区

COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机:人工智能

4区
NO NO

2022年12月升级版:

大类学科 小类学科 Top 综述期刊
计算机科学 4区

COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机:人工智能

4区
NO NO
《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 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.