Journal Title：Evolving Systems

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.

《進(jìn)化系統(tǒng)》涵蓋了動態(tài)進(jìn)化系統(tǒng)領(lǐng)域的調(diào)查、方法論和應(yīng)用導(dǎo)向論文。‘進(jìn)化系統(tǒng)’的靈感來自于動態(tài)變化和進(jìn)化環(huán)境中系統(tǒng)模型進(jìn)化的理念。與機(jī)器學(xué)習(xí)、數(shù)學(xué)建模和相關(guān)學(xué)科中的標(biāo)準(zhǔn)方法不同，這些方法假設(shè)并先驗地固定模型結(jié)構(gòu)，問題集中在參數(shù)優(yōu)化上，而進(jìn)化系統(tǒng)允許模型結(jié)構(gòu)逐漸改變/進(jìn)化。這種持續(xù)或終身學(xué)習(xí)和領(lǐng)域適應(yīng)的目的是自我組織。它可以適應(yīng)新的數(shù)據(jù)模式，更適合流數(shù)據(jù)、遷移學(xué)習(xí)，并且可以識別和學(xué)習(xí)未知和不可預(yù)測的數(shù)據(jù)模式。這些特性對于自主機(jī)器人系統(tǒng)至關(guān)重要，因為這些系統(tǒng)在設(shè)計完成后（運(yùn)行時）會繼續(xù)學(xué)習(xí)和適應(yīng)。

《Evolving Systems》征集的出版物旨在解決非平穩(wěn)、不可預(yù)測環(huán)境中系統(tǒng)建模、聚類、分類、預(yù)測和控制的各個方面的問題，并描述其設(shè)計的新方法和途徑。

該期刊致力于從系統(tǒng)方法到案例研究和實際工業(yè)應(yīng)用的自我開發(fā)、自我組織和進(jìn)化系統(tǒng)的主題。它涵蓋了方法論的各個方面，例如

不斷發(fā)展的系統(tǒng)方法

不斷發(fā)展的神經(jīng)網(wǎng)絡(luò)和神經(jīng)模糊系統(tǒng)

不斷發(fā)展的分類器和聚類

不斷發(fā)展的控制器和預(yù)測模型

不斷發(fā)展的可解釋人工智能系統(tǒng)

不斷發(fā)展的系統(tǒng)應(yīng)用

而且還關(guān)注新的范式和應(yīng)用，包括醫(yī)學(xué)、機(jī)器人、商業(yè)、工業(yè)自動化、控制系統(tǒng)、交通、通信、環(huán)境監(jiān)測、生物醫(yī)學(xué)系統(tǒng)、安全和電子服務(wù)、金融和經(jīng)濟(jì)。所有提交的方法和系統(tǒng)的共同特征是系統(tǒng)和環(huán)境的不斷發(fā)展。

該期刊涵蓋與以下內(nèi)容相關(guān)的貢獻(xiàn)：

1）機(jī)器學(xué)習(xí)、人工智能、計算智能和數(shù)學(xué)建模方法

2）來自自然和生物學(xué)的靈感，包括神經(jīng)科學(xué)、生物信息學(xué)和分子生物學(xué)、量子物理學(xué)

3）在工程、商業(yè)、社會科學(xué)中的應(yīng)用。

Evolving Systems由SPRINGER HEIDELBERG出版商出版，收稿方向涵蓋COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE全領(lǐng)域，此期刊水平偏中等偏靠后，在所屬細(xì)分領(lǐng)域中專業(yè)影響力一般，過審相對較易，如果您文章質(zhì)量佳，選擇此期刊，發(fā)表機(jī)率較高。平均審稿速度 ，影響因子指數(shù)2.7，該期刊近期沒有被列入國際期刊預(yù)警名單，廣大學(xué)者值得一試。