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Evolving Systems

Evolving Systems

SCISCIE

国际简称:EVOL SYST-GER  参考译名:不断发展的系统

  • 中科院分区

    3区

  • CiteScore分区

    Q1

  • JCR分区

    Q2

基本信息:
ISSN:1868-6478
E-ISSN:1868-6486
是否OA:未开放
是否预警:否
TOP期刊:否
出版信息:
出版地区:GERMANY
出版商:SPRINGER HEIDELBERG
出版语言:English
出版周期:6 issues per year
研究方向:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
评价信息:
影响因子:3.7
CiteScore指数:8.1
SJR指数:0.754
SNIP指数:1.191
发文数据:
Gold OA文章占比:8.42%
研究类文章占比:93.60%
年发文量:125
自引率:0.0625
开源占比:0.0356
出版撤稿占比:
出版国人文章占比:0
OA被引用占比:
英文简介 期刊介绍 CiteScore数据 中科院SCI分区 JCR分区 发文数据 常见问题

英文简介Evolving Systems期刊介绍

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.

期刊简介Evolving Systems期刊介绍

《Evolving Systems》是一本计算机科学优秀杂志。致力于发表原创科学研究结果,并为计算机科学各个领域的原创研究提供一个展示平台,以促进计算机科学领域的的进步。该刊鼓励先进的、清晰的阐述,从广泛的视角提供当前感兴趣的研究主题的新见解,或审查多年来某个重要领域的所有重要发展。该期刊特色在于及时报道计算机科学领域的最新进展和新发现新突破等。该刊近一年未被列入预警期刊名单,目前已被权威数据库SCI、SCIE收录,得到了广泛的认可。

该期刊投稿重要关注点:

Cite Score数据(2026年6月最新版)Evolving Systems Cite Score数据

  • CiteScore:8.1
  • SJR:0.754
  • SNIP:1.191
学科类别 分区 排名 百分位
大类:Mathematics 小类:Control and Optimization Q1 11 / 198

94%

大类:Mathematics 小类:Modeling and Simulation Q1 28 / 397

93%

大类:Mathematics 小类:Control and Systems Engineering Q1 67 / 390

82%

大类:Mathematics 小类:Computer Science Applications Q1 200 / 1022

80%

CiteScore 是由Elsevier(爱思唯尔)推出的另一种评价期刊影响力的文献计量指标。反映出一家期刊近期发表论文的年篇均引用次数。CiteScore以Scopus数据库中收集的引文为基础,针对的是前四年发表的论文的引文。CiteScore的意义在于,它可以为学术界提供一种新的、更全面、更客观地评价期刊影响力的方法,而不仅仅是通过影响因子(IF)这一单一指标来评价。

历年Cite Score趋势图

中科院SCI分区Evolving Systems 中科院分区

《新锐期刊分区表》(2026年3月发布) 综述期刊:否 Top期刊:否
大类学科 分区 小类学科 分区
计算机科学 3区 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机:人工智能 4区
期刊分区表(2025年3月升级版) 综述期刊:否 Top期刊:否
大类学科 分区 小类学科 分区
计算机科学 4区 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机:人工智能 4区
期刊分区表(2023年12月升级版) 综述期刊:否 Top期刊:否
大类学科 分区 小类学科 分区
计算机科学 4区 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机:人工智能 4区
期刊分区表(2022年12月升级版) 综述期刊:否 Top期刊:否
大类学科 分区 小类学科 分区
计算机科学 4区 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机:人工智能 4区
期刊分区表(2021年12月升级版) 综述期刊:否 Top期刊:否
大类学科 分区 小类学科 分区
计算机科学 4区 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机:人工智能 4区

中科院分区表 是以客观数据为基础,运用科学计量学方法对国际、国内学术期刊依据影响力进行等级划分的期刊评价标准。它为我国科研、教育机构的管理人员、科研工作者提供了一份评价国际学术期刊影响力的参考数据,得到了全国各地高校、科研机构的广泛认可。

中科院分区表 将所有期刊按照一定指标划分为1区、2区、3区、4区四个层次,类似于“优、良、及格”等。最开始,这个分区只是为了方便图书管理及图书情报领域的研究和期刊评估。之后中科院分区逐步发展成为了一种评价学术期刊质量的重要工具。

历年中科院分区趋势图

JCR分区Evolving Systems JCR分区

2025-2026年最新版
按JCI指标学科分区 收录子集 分区 排名 百分位
学科:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE SCIE Q2 98 / 210

53.6

学科:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE SCIE Q3 130 / 210

38.33

2023-2024年最新版
按JCI指标学科分区 收录子集 分区 排名 百分位
学科:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE SCIE Q3 101 / 197

49

学科:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE SCIE Q3 122 / 198

38.64

JCR分区的优势在于它可以帮助读者对学术文献质量进行评估。不同学科的文章引用量可能存在较大的差异,此时单独依靠影响因子(IF)评价期刊的质量可能是存在一定问题的。因此,JCR将期刊按照学科门类和影响因子分为不同的分区,这样读者可以根据自己的研究领域和需求选择合适的期刊。

历年影响因子趋势图

发文数据

国家/地区发文量统计
  • 国家/地区数量
  • Greece39
  • India28
  • Iran17
  • Algeria12
  • USA8
  • GERMANY (FED REP GER)7
  • Brazil6
  • England6
  • France6
  • Scotland6

本刊中国学者近年发表论文

  • 1、Multi-objective optimization for multi-UAV rendezvous planning in urban environment

    Author: Zhang, Lijing; Yu, Jiamin; Lu, Yulan; Sun, Xin; Si, Xinhui; Zhang, Hu

    Journal: EVOLVING SYSTEMS. 2026; Vol. 17, Issue 2, pp. -. DOI: 10.1007/s12530-026-09832-y

  • 2、Enhanced elite opposition-based manta ray foraging optimization for data clusterin

    Author: Cui, Xinhui; Yuan, Rongrong; Li, Wenxuan; Hu, Rui

    Journal: EVOLVING SYSTEMS. 2026; Vol. 17, Issue 2, pp. -. DOI: 10.1007/s12530-026-09821-1

  • 3、ECTL: deep prediction of learning emotion propagation in smart teachin

    Author: Shou, Zhaoyu; Chen, Jingquan; Yuan, Xiaohu; Wu, Ziyong

    Journal: EVOLVING SYSTEMS. 2026; Vol. 17, Issue 2, pp. -. DOI: 10.1007/s12530-026-09820-2

  • 4、Lightweight block cipher algorithm identification scheme based on transforme

    Author: Yuan, Ke; Zhao, Chenmeng; Sun, Hanlin; Zhou, Yuwei; Fan, Gaojuan; Jia, Chunfu

    Journal: EVOLVING SYSTEMS. 2026; Vol. 17, Issue 2, pp. -. DOI: 10.1007/s12530-026-09802-4

  • 5、Multi-level cross-view contrastive learning for enhanced item intention-aware recommender syste

    Author: Zhang, Shuqin; Wang, Dashuang; Xia, Yuanqing; Zhao, Hui; Liu, Jizhao

    Journal: EVOLVING SYSTEMS. 2026; Vol. 17, Issue 2, pp. -. DOI: 10.1007/s12530-025-09783-w

  • 6、Fractional Newton-derived optimizer: a novel metaheuristic for global optimization problem

    Author: Gu, Yangyang; Mao, Yaqian; Shen, Yuqiu; Wu, Haixin; Yu, Yue; Wang, Jianhong

    Journal: EVOLVING SYSTEMS. 2026; Vol. 17, Issue 2, pp. -. DOI: 10.1007/s12530-025-09787-6

  • 7、Carbon price volatility prediction using a hybrid CEEMDAN-attention-LSTM approac

    Author: Yao, Junxuan; Wang, Heping; Mao, Suzhen

    Journal: EVOLVING SYSTEMS. 2025; Vol. 16, Issue 3, pp. -. DOI: 10.1007/s12530-025-09704-x

  • 8、A boosted sine cosine algorithm with dimension learning-based search and terminal replacement mechanism for optimization and engineering application

    Author: Huang, Wei; Chen, Huiling; Heidari, Ali Asghar; Xu, Hupeng; Zhang, Qian; Lin, Yaoyao

    Journal: EVOLVING SYSTEMS. 2025; Vol. 16, Issue 2, pp. -. DOI: 10.1007/s12530-025-09683-z

投稿常见问题

通讯方式:TIERGARTENSTRASSE 17, HEIDELBERG, GERMANY, D-69121。