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ECGC in CEC 2019 : Special Session on Evolutionary Computation for Granular Computing in 2019 IEEE Congress on Evolutionary Computation (CEC2019)

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Link: http://www.cec2019.org/programs/special_sessions.html#cec-35
 
When Jun 10, 2019 - Jun 13, 2019
Where New Zealand
Submission Deadline Jan 7, 2019
Notification Due Mar 31, 2019
Final Version Due Mar 31, 2019
Categories    computer
 

Call For Papers

1. Introduction
With the over-flooding of big data, researchers and practitioners have started showing remarkable interest to explore the data space, and have considered that structuralized knowledge reasoning is an effective computational paradigm for dealing with big data tasks. Granular computing (GC) focuses on the knowledge representation and reasoning with information granules, and fuzzy sets and rough sets are two crucial branches of GC. Fuzzy set theory was introduced to represent concepts with ambiguous boundaries and to understand the processes of complex human reasoning. It has become a popular tool for the design of fuzzy classifiers. Rough set theory was presented to quantitatively analyze the uncertainty and to process incomplete knowledge. It can find a decision-making table between the strict statistics and random distribution. Since RST can typically describe the uncertainty of knowledge, it has been extensively used in data mining, knowledge discovery, and intelligent system. It is a promising line of work for the design of efficient granular computing model and method for handling big data.

The global search performed by evolutionary computation algorithms frequently provides a valuable complement to the local search of non-evolutionary methods, and combinations of granular computing and evolutionary computation often show particular promise in practice. Evolutionary computation for granular computing emphasizes the utility of different evolutionary algorithms to various facets of granular computing, ranging from theoretical analysis to real-life applications. The main motivation for applying evolutionary algorithms to granular computing tasks in the knowledge reasoning is that they are robust and adaptive search methods, which can perform a global search in the space of candidate solutions. It has been a hot trend to address the classical and new-emerging granular computing problems by using different evolutionary algorithms. The benefits of exploring the combination of granular computing and evolutionary computation in the knowledge reasoning scenario will have an impact in multiple research disciplines and industry domains, including transportation, communications, social network, medical health, and so on.

2. Aim and Scope
The goal of this special section aims at providing a specific opportunity to review the state-of-the-art of evolutionary computation for granular computing, and bringing together researchers in the relevant areas to discuss the latest progress, new research methodologies and potential research topics. The selected papers will be beneficial to both academia and industry, for delivering the latest research results and inspiring new directions to study.
The topics of interest include, but are not limited to:
Fuzzy sets method and system with evolutionary algorithm
Rough sets method and system with evolutionary algorithm
Probabilistic granules model with evolutionary algorithm
Shadowed sets model with evolutionary algorithm
Multi-objective evolutionary algorithm for granular computing
Evolutionary fuzzy deep neural network for data classification
Granular computing framework for big data analytic by evolutionary algorithm
Evolutionary multimodal optimization for fuzzy rough system
Evolutionary multimodal optimization for rough fuzzy system
Quantum-inspired evolutionary algorithm for granular computing
Co-evolutionary algorithm for granular computing framework
Adaptive granular computing framework with evolutionary algorithm
Convergence analysis of evolutionary algorithm for granular computing
Evolutionary optimization with dynamic parameter adaptation for fuzzy system
Granular data mining for feature learning, classification, regression, and clustering with evolutionary algorithm
Granular data mining for multi-task modeling, multi-view modeling and co-learning with evolutionary algorithm
Real-world applications using evolutionary granular computing methods


3. Submissions
Papers should be submitted following the instructions at the IEEE CEC 2019 web site. Please select the main research topic as the Special Session on “Evolutionary Computation for Granular Computing”. All papers accepted and presented at CEC2019 will be included in the conference proceedings published by IEEE Explore, which are typically indexed by EI.
Information on the format and templates for papers can be found here:
http://www.cec2019.org/papers.html#templates

4. Important dates
• Paper submission: 7 January, 2019
• Decision notification: 7 March, 2019
• Camera ready paper due: 31 March, 2019
• Registration: 31 March, 2019
• Conference: 10 June, 2019

5. Session Organizers
Weiping Ding
Nantong University, China.
Email address: dwp9988@hotmail.com

Gary G. Yen
Oklahoma State University, U.S.A.
Email address: gyen@okstate.edu

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