IEEE Symposium on

Model-Based Evolutionary Algorithms

The IEEE Symposium on Model Based Evolutionary Algorithms (IEEE MBEA 2018) will be held simultaneously with other symposia and workshops in one location at the 2018 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2018). 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 2018 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 2018 proceedings and on IEEEXplore, conditioned on registering and presenting the paper at the conference.

The manuscripts should be submitted in PDF format. Click Here to know further guidelines for submission.


IEEE IEEE MBEA 2018 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:
  •    Estimation of distribution algorithms
  •    Evolutionary learning
  •    Evolutionary artificial neural networks
  •    Bayesian optimization algorithms
  •    CMA-ES
  •    Bare-bones particle swarm optimization
  •    Bare-bones differential evolution
  •    Regularity analysis for multi-objective optimization
  •    Inverse modelling for multi-objective optimization
  •    Pareto front reconstruction for multi-objective optimization
  •    Objective reduction for many-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

Accepted Special Sessions

  • Data-Driven Evolutionary Optimization of Computationally Expensive Problems
    • Organizers:
      Chaoli Sun, Taiyuan University of Science and Technology, China
      Jonathan Fieldsend, University of Exeter, UK
      Yew-Soon Ong, Nanyang Technological University, Singapore
    • More Information

Symposium Co-Chairs

Ran Cheng

University of Birmingham, UK

Aimin Zhou
East China Normal University, China

Jose A. Lozano
University of the Basque Country, Spain

Yaochu Jin
University of Surrey, UK.

Program Committee

Abishai DanielIntel, U.S.
Alex MendiburuUniversity of the Basque Country, Spain
Bas SteinLeiden University, Netherland
Bin LiuNanjing University of Posts and Telecommunications, China
Bing XueVictoria University of Wellington, New Zealand
Chaoli SunTaiyuan University of Science & Technology, China
Cong LiuUniversity of Shanghai for Science and Technology, China
Handing WangUniversity of Surrey, U.K.
Hao WangLeiden University, Netherland
Hemant SinghUniversity of New South Wales, Australia
Jiahai WangSun Yat-sen University, China
Jianhua XiaoNankai University, China
Jinyuan ZhangEast China Normal University, China
Joseph Chrol-CannonUniversity of Surrey, U.K.
Junfeng ChenHohai University, China
Kai QinRMIT University, Australia
Karthik SindhyaUniversity of Jyväskylä, Finland
Ke LiUniversity of Exeter, U.K.
Liangli ZhenSichuan University, China
Miqing LiUniversity of Birmingham, U.K.
Rui WangNational University of Defense Technology, China
Samineh BagheriCologne University of Applied Sciences, Germany
Spencer ThomasNational Physical Laboratory, U.K.
Thomas BäckLeiden University, Netherland
Tinkle ChughUniversity of Jyvaskyla, Finland
Weiguo ShengZhejiang University of Technology, China
Wenyin GongChina University of Geosciences, China
Xingyi ZhangAnhui University, China
Yi MeiVictoria University of Wellington, New Zealand
Ying-Ping ChenNational Chiao Tung University, Taiwan