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2019 CEC Special Session on:
"Probability Distribution Based Evolutionary Algorithms"

IEEE Congress on Evolutionary Computation 2019
J10-13 JUNE 2019, WELLINGTON, NEW ZEALAND
http://cec2019.org/

Important Dates
Submission Deadline:
January 7, 2019
Notification of Acceptance:
March 7, 2019

Estimation of Distribution Algorithm (EDA) is a special kind of evolutionary algorithm that works by constructing a probability model to estimate the distribution of the predominant individuals in the population. With this probability distribution based search behavior, EDA is good at maintaining search diversity, if suitable probability distribution models are chosen. In addition, the concept of EDA is applicable in both continuous and discrete search space, and shows robustness in complicated optimization problems with noise and uncertainty. In a border sense, there are also some other evolutionary computation (EC) or swarm intelligence (SI) algorithms that work by implicitly constructing a probability distribution in the solution space. For example, in ant colony optimization (ACO), ants deposit pheromone on paths, which can be seen as an implicit probability model. Such implicit probability distribution construction behavior provides a more feasible way to build promising probability distribution models, and thus further extends the concept of Probability Distribution Based Evolutionary Algorithms. There are a lot of applications in probability distribution based evolutionary algorithms, for example, task scheduling, routing, mixed-variable optimization, etc. Constructing suitable probability distribution models and studying the theory behind the probability distribution are important for the research of probability distribution based evolutionary algorithms, which are promising in solving such real-world applications.

The aim of this special session is to promote the research on theories and applications in this filed.

Scope and Topics

Topics of interest include, but are not limited to, EDA in the following aspects:

  • Estimation of Distribution Algorithms (EDAs)
  • Bayesian Optimization
  • Construction Methods of Explicit and Implicit Probability Distribution
  • Basic Theories of Probability Distribution Based Evolutionary Algorithms
  • Applications of Probability Distribution Based Evolutionary Algorithms
  • Distributed and Parallel Probability Distribution Based Evolutionary Algorithms
  • Probability Distribution Based Evolutionary Algorithms in Optimization under uncertainty
  • Multi-objective Probability Distribution Based Evolutionary Algorithms
  • Probability Distribution Based Evolutionary Algorithms for Mixed Variable Optimization

Paper Submission:

Papers for IEEE CEC 2019 should be submitted electronically through the Congress website at
http://cec2019.org/,
and will be refereed by experts in the fields and ranked based on the criteria of originality, significance, quality and clarity. To submit your papers to the special session, please select the Special Session name in the Main Research topic.

For more submission information please visit: http://cec2019.org/
All accepted papers will be published in the IEEE CEC 2019 electronic proceedings, included in the IEEE Xplore digital library, and indexed by  EI Compendex.

Organizer

Wei-Neng Chen
South China University of Technology, China
E-mail: cwnraul634@aliyun.com
Wei-Neng Chen (S'07-M'12-SM'17) received the Bachelor's degree and the Ph.D. degree from Sun Yat-sen University, Guangzhou, China, in 2006 and 2012, respectively. He is currently a professor with the School of Computer Science and Engineering, South China University of Technology, Guangzhou, China. His current research interests include swarm intelligence algorithms and their applications on cloud computing, operations research and software engineering. Dr. Chen has published more than 100 papers in international journals and conferences, including more than 20 papers in IEEE Transactions journals. His doctoral thesis received the IEEE Computational Intelligence Society (CIS) Outstanding Dissertation Award in 2016.He also received the National Science Fund for Excellent Young Scholars in 2016.

Jinghui Zhong
South China University of Technology, China
E-Mail: jinghuizhong@scut.edu.cn
Jinghui Zhong is currently an Associate Professor in the School of Computer Science and Engineering, South China University of Technology, Guangzhou, China. He received his Ph.D. degree from the School of Information Science and Technology, Sun YAT-SEN University, China, in 2012. During 2013 to 2016, He worked as a Postdoctoral Research Fellow at the School of Computer Engineering, Nanyang Technological University, Singapore. His research interests include evolutionary computation such as genetic programming, and the applications of evolutionary computation.

Zhi-Hui Zhan
South China University of Technology, China
E-Mail: zhanapollo@163.com
Zhi-Hui Zhan received the Bachelor's degree and the Ph. D degree in 2007 and 2013, respectively, from the Department of Computer Science of Sun Yat-Sen University, Guangzhou, China. He is currently a professor with School of Computer Science and Engineering, South China University of Technology, China. He is also appointed as the Pearl River Scholar Young Professor in 2016. His current research interests include evolutionary computation algorithms, swarm intelligence algorithms, and their applications in real-world problems, and in environments of cloud computing and big data. Dr. Zhan's doctoral dissertation was awarded the China Computer Federation Outstanding Dissertation and the IEEE CIS Outstanding Dissertation. Dr. Zhan received the Natural Science Foundation for Distinguished Young Scientists of Guangdong Province, China in 2014, awarded the Pearl River New Star in Science and Technology in 2015, awarded the Youth Talent in Science and Technology Innovation of Guangdong Province in 2016, and awarded the Wu Wen Jun Artificial Intelligence Excellent Youth from the Chinese Association for Artificial Intelligence in 2017. Dr. Zhan is listed as one of the Most Cited Chinese Researchers in Computer Science.

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