Home » Keynotes

Organized by

 

Keynotes

Title:Data-driven evolutionary optimization:A taxonomy and case studies

Professor Yaochu Jin, University of Surrey, United Kingdom.

Yaochu Jin received the B.Sc., M.Sc., and Ph.D. degrees from Zhejiang University, Hangzhou, China, in 1988, 1991, and 1996, respectively, and the Dr.-Ing. degree from Ruhr University Bochum, Germany, in 2001.
He is a Professor in Computational Intelligence, Department of Computer Science, University of Surrey, Guildford, U.K., where he heads the Nature Inspired Computing and Engineering Group. He is also a Finland Distinguished Professor funded by the Finnish Agency for Innovation (Tekes) and a Changjiang Distinguished Visiting Professor appointed by the Ministry of Education, China. His main research interests include data-driven surrogate-assisted evolutionary optimization, evolutionary multi-objective optimization, evolutionary learning, interpretable and secure machine learning, and evolutionary developmental systems. He has (co)authored over 250 peer-reviewed journal and conference papers and been granted eight patents on evolutionary optimization. He has delivered 30 invited keynote speeches at international conferences.
Dr Jin is the Editor-in-Chief of the IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS and Co-Editor-in-Chief of Complex & Intelligent Systems. He is an IEEE Distinguished Lecturer (2013-2015 and 2017-2019) and past Vice President for Technical Activities of the IEEE Computational Intelligence Society (2014-2015). He is the recipient of the 2018 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, the 2015 and 2017 IEEE Computational Intelligence Magazine Outstanding Paper Award, and the Best Paper Award of the 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. He is a Fellow of IEEE.
Email: yaochu.jin@surrey.ac.uk
Personal webpage: https://www.surrey.ac.uk/people/yaochu-jin 

 

Title:Modeling and evolutionary heuristic solutions for some operations planning problems

Professor Jiyin Liu, Loughborough University, United Kingdom

Jiyin Liu is a Professor of Operations Management in the School of Business and Economics at Loughborough University. He works in both operations management and operational research areas. Jiyin received his PhD in Manufacturing Engineering and Operations Management from the University of Nottingham in 1993 and lectured at Hong Kong University of Science and Technology before joining Loughborough at the end of 2003.  He is also a Chang Jiang Scholar Chair Professor at Northeastern University of China since 2007.
Jiyin’s research is mainly on modelling and optimisation of operations planning problems in logistics and production systems. His research outputs have been published in leading academic journals in the areas of operational research and operations management, such as Operations Research, European Journal of Operational Research, Transportation Research Part B, Manufacturing & Service Operations Management, Naval Research Logistics, International Journal of Production Research, IIE Transactions, and IEEE Transactions.
Jiyin focuses on problems with both academic significance and practical relevance. His work has been supported by research funding agencies and by industry. He has collaborated with companies such as Baosteel, British Telecom, Hongkong International Terminals, Hong Kong Air Cargo Terminals Limited, and Philips Electronics. He received the Franz Edelman Finalist Awards from INFORMS for Achievements in Practice of Operations Research and Management Sciences twice for works on decision support in container terminal operations (2004) and in steel industry (2013), respectively.
Email: J.Y.Liu@lboro.ac.uk
Personal webpage: http://www.lboro.ac.uk/departments/sbe/staff/jiyin-liu/

 

Professor Thom LaBean, North Carolina State University, USA

LaBean’s interests include molecular materials, biomolecular engineering, bionano science, molecular self-assembly.
LaBean earned his BS in biochemistry from the Honors College at Michigan State University, 1985, and his PhD in biochemistry from the University of Pennsylvania, 1993. He studied protein design as a postdoctoral fellow at Duke University. Recently, he ran his own group as research professor with appointments in the departments of Computer Science, Chemistry, and Biomedical Engineering at Duke University. LaBean joined the Materials Science and Engineering Faculty in August 2011. Throughout his career, LaBean has studied the structure, evolution, and engineering of biopolymers (biomacromolecules and materials assembled from them). Current research projects involve the design, construction, and testing of self-assembling DNA nanostructures for applications in molecular materials, nanoelectronics, nanophotonics, molecular robotics, and nanomedicine. Potential applications include the further miniaturization of electronics circuits and devices, creation of stimulus responsive constructs for chemo- and bio-sensing, and molecular therapeutics with inherent computational functin.
Email: thlabean@ncsu.edu
Personal webpage: https://www.mse.ncsu.edu/profile/thlabean

 

Title:Deep Reinforcement Learning and Game AIs

Professor Dongbin Zhao,Chinese Academy of Sciences,China

Dongbin Zhao is a professor of Institute of Automation, Chinese Academy of Sciences, and the University of Chinese Academy of Sciences, China. He was also a visiting scholar at the University of Arizona. He has published 4 books, and over 60 international journal papers. His current research interests are in the area of deep reinforcement learning, computational intelligence, adaptive dynamic programming, autonomous driving, robotics, intelligent transportation systems, and smart grids.
Dr. Zhao is an IEEE senior member. He serves as the Associate Editor of IEEE Transactions on Neural Networks and Learning Systems (2012-), IEEE Computation Intelligence Magazine (2014-), IEEE Transactions on Cybernetics (2017-), etc. He is the Chair of Beijing Chapter, and was the Chair of Adaptive Dynamic Programming and Reinforcement Learning (ADPRL) Technical Committee (2015-2016), Multimedia Subcommittee (2015-2016) of IEEE Computational Intelligence Society (CIS). He works as several guest editors of renowned international journals, including the Lead Chair of IEEE TNNLS special issue on Deep Reinforcement Learning and Adaptive Dynamic Programming, and IEEE CIM special issue on Deep Reinforcement Learning and Games. He is involved in organizing many international conferences, as follows. He serves as the Program Chair of IEEE Symposium on ADPRL from 2016 to 2018, Program Chair of International Conference on Neural Information Processing (ICONIP 2017), and Tutorial Chair of ICONIP 2018, Program Chair of International Conference on Information Science and Technology (ICIST) 2018 and 2015, International Conference on Intelligent Control and Information Processing (ICICIP 2013). He serves as the Program Co-Chair of World Congress on Intelligent Control and Automation (WCICA 2016), and International Symposium on Neural Networks (ISNN 2015), and International Workshop on Computational Energy Management in Smart Grids (CEMiSG) from 2015 to 2018. He also serves as the Publicity Co-chair of IEEE World Congress on Computational Intelligence (WCCI) 2016, and Finance Co-chair of WCCI 2014, and the Post Chair of IEEE Symposiums Series on Computational Intelligence (SSCI 2014).
Email: dongbin.zhao@ia.ac.cn
Personal webpage: http://people.ucas.ac.cn/~zhaodongbin

 

Title:Production, Logistics and Energy Optimization and Application in Steel Industry

Abstract: This talk discusses some interesting topics on the scheduling and data analytics of production, logistics and energy in the steel industry, including: 1) production scheduling in steel-making and hot/cold rolling operations; 2) logistics scheduling in storage/stowage, shuffling, transportation and (un)loading operations; 3) energy optimization including energy allocation and coordinated planning and scheduling of production and energy; 4) data based analytics including dynamic analytics of BOF steelmaking process based on multi-stage modeling; temperature prediction of blast furnace; temperature prediction of molten iron in transportation process; energy analytics for estimation, prediction of generation and consumption, diagnosis and benchmarking; temperature prediction of reheat furnace based on mechanism and data; strip quality analytics of continuous annealing based on multi-objective ensemble learning; process monitoring and diagnosis of continuous annealing based on mechanism and data.

Professor Lixin Tang,Northeastern University,China

Lixin Tang is a Cheung Kong Scholars Chair Professor, the Vice President of Northeastern University, the Director of the Institute of Industrial & Systems Engineering, and the Head of the Operation Analytics and Optimization Centre for Smart Industry at Northeastern University of China.

His research interests cover plant-wide production and logistics planning, production and logistics batching and scheduling, operations analytics and optimization for smart industry, convex and integer optimization, data analytics and machine learning, computational intelligent optimization and engineering applications in manufacturing (steel, petroleum-chemical, nonferrous), energy, resources industry and logistics systems.

He has published 106 papers in international journals such as OR, M&SOM, INFORMS Journal on Computing, IISE Transactions, NRL, IEEE Transactions on Evolutionary Computation. He was selected into the list of 2014, 2015 and 2016 Most Cited Chinese Researchers by Elsevier. The paper published on flagship journal IIE Transactions (now renamed as IISE Transactions) won the Best Applications Paper Award of 2015-2016.

He serves as an Associate Editor of IISE Transactions, IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, IEEE Transactions on Automation Science and Engineering, Journal of Scheduling, International Journal of Production Research, Journal of the Operational Research Society, in Editorial Board of Annals of Operations Research, and an Area Editor of the Asia-Pacific Journal of Operational Research.

 

Title:Decomposition Based Multiobjective Evolutionary Computation: the current state and future

Professor Qingfu Zhang ,City University of Hong Kong,Hong Kong

Professor, IEEE Fellow, Department of Computer Science, City University of Hong Kong, Hong Kong
Changjiang Visiting Chair Professor at Xidian University, China, 2011.
Expert in one thousand talent program of China, 2015.
Highly cited researcher in computer science, 2016, 2017
Email: qingfu.zhang@cityu.edu.hk.
Personal homepage: http://www.cs.cityu.edu.hk/~qzhang/index.html

Important Dates


Paper Submission Deadline
July 1, 2018  July 22, 2018
Notification of Paper Acceptance
August 1, 2018
Final Paper Submission
August 15, 2018
Conference Date
November 2 – 4, 2018

温馨提示
所有稿件的最终结果、审稿意见和注册信息都已经发送完毕,若有任何作者没收到这些信息请邮件联系
赵老师:zhaoxc@bupt.edu.cn;
潘老师:lqpanhust@gmail.com
The final results of all manuscripts, review comments and registration information have been sent out. If any authors do not receive these messages, please email to
Xinchao Zhao: zhaoxc@bupt.edu.cn;
Linqiang Pan: lqpanhust@gmail.com

各位老师同学好:
高影响力SCI期刊SWEC BIC-TA2018 会议专刊征集已经上线,在杂志官网虚位以待,敬请各位投稿等推荐。 https://www.journals.elsevier.com/swarm-and-evolutionary-computation/call-for-papers/advances-theory-application-bio-inspired-swarm