2019 IEEE Symposium Series on Computational Intelligence


IEEE Symposium Series on Computational Intelligence

December 6-9, 2019 Xiamen, China

 

IEEE Symposium on Model Based Evolutionary Algorithms (IEEE MBEA)

The IEEE Symposium on Model Based Evolutionary Algorithms (IEEE MBEA’2019) will be held simultaneously with other symposia and workshops in one location at the 2019 IEEE Symposium Series on Computational Intelligence (IEEE SSCI’2019). This international event promotes all aspects of the theory and applications of computational intelligence. Sponsored by the IEEE Computational Intelligence Society, this event will attract top researchers, professionals, practitioners and students from around the world. The registration to SSCI 2019 will allow participants to attend all the symposia, including the complete set of the proceedings of all the meetings, coffee breaks, lunches, and the banquet.

Accepted papers will be published in the IEEE SSCI 2019 proceedings and on IEEEXplore, conditioned on registering and presenting the paper at the conference.

Topics

IEEE MBEA’2019 aims to bring together scientists, engineers and students from around the world to discuss the latest advances in the field of machine learning related techniques applied to evolutionary computation, such as theories, algorithms, systems and applications are welcome; these include, but are not limited to:

  • CMA-ES
  • Estimation of distribution algorithms
  • Bayesian optimization algorithms
  • Evolutionary artificial neural networks
  • Deep learning and its applications
  • Bare-bones particle swarm optimization
  • Bare-bones differential evolution
  • Inverse modelling for multi-objective optimization
  • Pareto front reconstruction for multi-objective optimization
  • Surrogate-assisted evolutionary computation for computationally expensive problems
  • Surrogate models management in evolutionary computation
  • Adaptive sampling using machine learning and statistical techniques
  • Data-driven optimization using big data and data analytics
  • Evolutionary dynamic optimization
  • Multifactorial optimization in evolutionary multitasking

Import Dates:

April 1, 2019: Special Session Proposal
July 10, 2019: Full Paper Submissions
Sept 1, 2019: Notification of acceptance
Oct 1, 2019: Final Version and Early Registration
Dec 6-9, 2019: Conference Dates

Accepted Special Sessions


Title:Data-Driven Evolutionary Optimization of Computationally Expensive Problems

Special Session Chairs

Chaoli Sun, Department of Computer Science and Technology, Taiyuan University of Science and Technology, Shanxi China
Email: chaoli.sun.cn@gmail.com, chaoli.sun@tyust.edu.cn
Homepage

Jonathan Fieldsend, Department of Computer Science, University of Exeter, Devon EX4, UK
Email: J.E.Fieldsend@exeter.ac.uk
Homepage

Yew-Soon Ong, School of Computer Engineering, Nanyang Technological University, Block N4, 2a-28, Nanyang Avenue, Singapore
Email: ASYSOng@ntu.edu.sg
Homepage

Handing Wang, Department of Computer Science, University of Surrey, Guildford, GU27XH, UK
Email: handing.wang@surrey.ac.uk
Homepage

Content

Meta-heuristic algorithms, including evolutionary algorithms and swarm optimization, face challenges when solving time-consuming problems, as typically these approaches require thousands of function evaluations to arrive at solutions that are of reasonable quality. Surrogate models, which are computationally cheap, have in recent years gained in popularity in assisting meta-heuristic optimization, by replacing the compute-expense/time-expensive problem during phases of the heuristic search. However, due to the curse of dimensionality, it is very difficult, if not impossible to train accurate surrogate models. Thus, appropriate model management techniques, memetic strategies and other schemes are often indispensable. In addition, modern data analytics involving advance sampling techniques and learning techniques such as semi-supervised learning, transfer learning and active learning are highly beneficial for speeding up evolutionary search while bringing new insights into the problems of interest. This special session aims at bringing together researchers from both academia and industry to explore future directions in this field.

Scope and Topics

Submissions are expected from, but not limited to the following topics:

  • Surrogate-assisted evolutionary optimization for computationally expensive problems
  • Adaptive sampling using machine learning and statistical techniques
  • Surrogate model management in evolutionary optimization
  • Data-driven optimization using big data and data analytics
  • Knowledge acquisition from data and reuse for evolutionary optimization
  • Computationally efficient evolutionary algorithms for large scale and/or many-objective optimization problems
  • Real world applications including multidisciplinary optimization

Symposium Chairs

Ran Cheng

Southern University of Science and Technology, China.

Email: chengr@sustc.edu.cn

Homepage

Cheng He

Southern University of Science and Technology, China.

Email: chenghehust@gmail.com

Homepage

Jose A. Lozano

University of the Basque Country, Spain.

Email: ja.lozano@ehu.es

Homepage

Yaochu Jin

University of Surrey, UK.

Email: yaochu.jin@surrey.ac.uk

Homepage

Program Committee

Abishai Daniel Intel, U.S.
Alex Mendiburu University of the Basque Country, Spain
Bas Stein Leiden University, Netherland
Bing, Xue Victoria University of Wellington, New Zealand
Chaoli Sun Taiyuan University of Science & Technology, China
Cong Liu University of Shanghai for Science and Technology, China
Han Huang South China University of Technology, China
Handing Wang Xidian University, China
Hao Wang Leiden University, Netherland
Hemant Singh University of New South Wales, Australia
Jiahai Wang Sun Yat-sen University, China
Jianhua Xiao Nankai University, China
Jinyuan Zhang East China Normal University, China
Joseph Chrol-Cannon University of Surrey, U.K.
Junfeng Chen Hohai University, China
Kai Qin RMIT University, Australia
Karthik Sindhya University of Jyv盲skyl盲, Finland
Ke Li University of Exeter, U.K.
Liangli Zhen Sichuan University, China
Lianghao Li Huazhong University of Science and Technology, China
Miqing Li University of Birmingham, U.K.
Rui Wang National University of Defense Technology, China
Samineh Bagheri Cologne University of Applied Sciences, Germany
Spencer Thomas National Physical Laboratory, U.K.
Thomas B盲ck Leiden University, Netherland
Tinkle Chugh University of Jyvaskyla, Finland
Weian Guo Tongji University, China
Weiguo Sheng Zhejiang University of Technology, China
Wenyin Gong China University of Geosciences, China
Xingyi Zhang Anhui University, China
Yi Mei Victoria University of Wellington, New Zealand
Ying-Ping Chen National Chiao Tung University, Taiwan
Yujun Zheng Zhejiang University of Technology, China