Journal Information
Applied Soft Computing
http://www.journals.elsevier.com/applied-soft-computing/
Impact Factor:
6.725
Publisher:
Elsevier
ISSN:
1568-4946
Viewed:
20407
Tracked:
37
Call For Papers
Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems. Soft computing is a collection of methodologies, which aim to exploit tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities.

Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.

Major Topics:

The scope of this journal covers the following soft computing and related techniques, interactions between several soft computing techniques, and their industrial applications:

• Fuzzy Computing
• Neuro Computing
• Evolutionary Computing
• Probabilistic Computing
• Immunological Computing
• Hybrid Methods
• Rough Sets
• Chaos Theory
• Particle Swarm
• Ant Colony
• Wavelet
• Morphic Computing

The application areas of interest include but are not limited to:

• Decision Support
• Process and System Control
• System Identification and Modelling
• Engineerin Design Optimisation
• Signal or Image Processing
• Vision or Pattern Recognition
• Condition Monitoring
• Fault Diagnosis
• Systems Integration
• Internet Tools
• Human-Machine Interface
• Time Series Prediction
• Robotics
• Motion Control and Power Electronics
• Biomedical Engineering
• Virtual Reality
• Reactive Distributed AI
• Telecommunications
• Consumer Electronics
• Industrial Electronics
• Manufacturing Systems
• Power and Energy
• Data Mining
• Data Visualisation
• Intelligent Information Retrieval
• Bio-inspired Systems
• Autonomous Reasoning
• Intelligent Agents
• Multi-objective Optimisation
• Process Optimisation
• Agricultural Machinery and Produce
• Nano and Micro-systems
Last updated by Dou Sun in 2022-01-29
Special Issues
Special Issue on Neuroevolution Techniques: Methods and Applications
Submission Date: 2023-01-31

In the last years, the huge increase of complex and heterogeneous data to address increasingly challenging problems has given rise to novel computational concepts and techniques. Neuroevolution, inspired by the fact that natural brains themselves are the products of an evolutionary process, belongs to these emerging techniques. It combines the search ability of evolutionary computation with the learning capability of artificial neural networks. The recent development of deep learning techniques to tackle complex problems has given a further impetus to the request for evolving and optimizing artificial neural networks through evolutionary computation. Neuroevolution has been successfully applied to many domains including strategy games, image processing and computer vision, text mining and natural language processing, speech processing, software engineering, time series analysis, cybersecurity, finance and fraud detection, social networks, recommender systems, evolutionary robotics, big data, healthcare, biomedicine and bioinformatics. The reason behind its success lies in important capabilities that are typically unavailable to traditional approaches, including evolving neural network building blocks, hyperparameters, architectures and even the algorithms for learning themselves (meta-learning). Although promising, the use of neuroevolution poses important problems and challenges for its future developments. Firstly, many of its paradigms suffer from lack of parameter-space diversity, meaning with this a failure in providing diversity in the behaviors generated by the different networks. Moreover, the harnessing of neuroevolution to optimize deep neural networks requires noticeable computational power and, consequently, the investigation of new trends in enhancing the computational performance.
Last updated by Dou Sun in 2022-09-11
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