期刊簡介預計審稿時間：

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.

《進化系統(tǒng)》涵蓋了動態(tài)進化系統(tǒng)領(lǐng)域的調(diào)查、方法論和應用導向論文。‘進化系統(tǒng)’的靈感來自于動態(tài)變化和進化環(huán)境中系統(tǒng)模型進化的理念。與機器學習、數(shù)學建模和相關(guān)學科中的標準方法不同，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，而進化系統(tǒng)允許模型結(jié)構(gòu)逐漸改變/進化。這種持續(xù)或終身學習和領(lǐng)域適應的目的是自我組織。它可以適應新的數(shù)據(jù)模式，更適合流數(shù)據(jù)、遷移學習，并且可以識別和學習未知和不可預測的數(shù)據(jù)模式。這些特性對于自主機器人系統(tǒng)至關(guān)重要，因為這些系統(tǒng)在設(shè)計完成后（運行時）會繼續(xù)學習和適應。

《Evolving Systems》征集的出版物旨在解決非平穩(wěn)、不可預測環(huán)境中系統(tǒng)建模、聚類、分類、預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)應用的自我開發(fā)、自我組織和進化系統(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)應用

而且還關(guān)注新的范式和應用，包括醫(yī)學、機器人、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)學系統(tǒng)、安全和電子服務(wù)、金融和經(jīng)濟。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：

1）機器學習、人工智能、計算智能和數(shù)學建模方法

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

3）在工程、商業(yè)、社會科學中的應用。

《Evolving Systems》(不斷發(fā)展的系統(tǒng))編輯部通訊方式為TIERGARTENSTRASSE 17, HEIDELBERG, GERMANY, D-69121。如果您需要協(xié)助投稿或潤稿服務(wù)，您可以咨詢我們的客服老師。我們專注于期刊咨詢服務(wù)十年，熟悉發(fā)表政策，可為您提供一對一投稿指導，避免您在投稿時頻繁碰壁，節(jié)省您的寶貴時間，有效提升發(fā)表機率，確保SCI檢索（檢索不了全額退款）。我們視信譽為生命，多方面確保文章安全保密，在任何情況下都不會泄露您的個人信息或稿件內(nèi)容。