Journal Information
Soft Computing
http://www.springer.com/engineering/computational+intelligence+and+complexity/journal/500
Impact Factor:
1.63
Publisher:
Springer
ISSN:
1432-7643
Viewed:
4289
Tracked:
7

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Call For Papers
Soft Computing provides rapid dissemination of important results in soft computing foundations, methodologies and applications. It encourages the integration of soft computing theoretical and practical results into both everyday and advanced applications. The journal aims to connect the ideas and techniques of soft computing with other disciplines.

Soft Computing is dedicated to system solutions based on soft computing paradigms. It provides rapid dissemination of important results in soft computing, a fusion of research in evolutionary algorithms, genetic programming, swarm intelligence, neural science, neural net systems, fuzzy set theory, fuzzy systems, Bayesian networks, chaos theory, chaotic systems.

By linking the ideas and techniques of soft computing with other disciplines, the journal serves as a unifying platform that fosters comparisons, extensions, and new applications. As a result, the journal is an international forum for all scientists engaged in research and development in this fast growing field.

Soft Computing - Section Foundations

The Section Foundations welcomes original and significant contributions to the mathematical and logical foundations of soft computing, and related areas.

The Section is a forum that aims at promoting, circulating, and stimulating the research on mathematical foundations of soft computing intended in a broad sense as a method for dealing with non crisp information, for instance characterized by the presence of vagueness, imprecisions, high complexity, or uncertainty.

Relevant topics include, but are not limited to,

    Algebra and algebraic logic,
    Computational paradigms and computational complexity
    Description logic, temporal logic, dynamic logic, and modal logic
    Domain theory and type theory
    Fuzzy logic, fuzzy set theory, and many-valued logic
    Substructural logic
    Probability logic, belief functions, etc.

Soft Computing – Section Methodologies and Applications

The Section M&A aims at promoting and stimulating the research in the field of the development of enhanced computational systems based on innovative or consolidated soft computing methodologies such as Fuzzy Logic, Neural Networks and Evolutionary Algorithms. Under this perspective, particular attention is given to hybrid and agent-based systems i.e., computational frameworks that use different soft computing approaches in a synergic way in order to face the intrinsic complexities characterizing real environments and exploit imprecise and vague information which strongly characterizes real-world scenarios. As consequence, this journal section seeks original research contributions with a focus on applications belonging but not limited to following areas:

    computer networks,
    data mining,
    image and video processing,
    intelligence agents,
    machine learning,
    pattern recognition,
    robotics,
    web intelligence.
Last updated by Dou Sun in 2016-08-12
Special Issues
Special Issue on Optimization with Uncertain Information: A Perspective of Soft Computing
Submission Date: 2017-03-30

There are a number of focal points of the issue that make this project coherent, justifiable and relevant. In what follows, we concisely identify the main topics along with a few lines of justification. 1.Evolutionary computing Evolutionary computing, inspired by the biological mechanism of evolution, is a global optimization method with heuristic or stochastic optimization character. They are mostly used for black box problems (no derivatives known), often in the context of expensive optimization. Uncertain optimization is one of these black box problems. 2.Neural networks Neural networks are a family of statistical learning algorithms inspired by biological neural networks and are used to approximate functions that can depend on a large number of inputs and are generally unknown. Uncertain optimization system is essential an unknown function with numerous inputs. 3.Soft computing + Forecasting + Uncertain optimization The topics of soft computing, forecasting and uncertain optimization are highly related. Soft computing is the basic of forecasting and uncertain optimization methods. And forecasting information can improve the performance of optimality. Here, we mainly concern the soft computing-based forecasting methods, soft computing-based uncertain optimization methods, and forecasting information-based uncertain optimization methods. 4.Uncertain optimization + Big data Big data analysis is the first step in the solution of problem. Acting on the analysis results to make good decisions via optimization methods is the critical next step. During this process, innovative soft computing, forecasting and uncertain analysis methods would be respectively used to handle the high volume, high velocity and high variety characteristics of big data. 5.Innovative applications The state-of-the-art case studies include design and analysis of transportation networks, logistics and supply chains, hazardous materials transportation, portfolio management and optimization, risk management, robust design, network reliability, software reliability, redundancy optimization, decision support and so on.
Last updated by Dou Sun in 2016-11-20
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