IEEE Symposium on Model Based Evolutionary Algorithms (IEEE MBEA)

IEEE Symposium on Model Based Evolutionary Algorithms (IEEE MBEA)

IEEE MBEA’2022 aims to bring together scientists, engineers and students from around the world to discuss the latest advances in the interdisciplinary research on evolutionary computation using machine learning techniques/methods.

Topics

Detailed research topics include, but are not limited to:
  • CMA-ES
  • Estimation of Distribution Algorithms
  • Bare-bones Metaheuristic Algorithms
  • Bayesian Optimization Algorithms
  • Surrogate Assisted Evolutionary Algorithms
  • Reinforcement Learning Assisted Evolutionary Algorithms
  • Transfer Learning Assisted Evolutionary Algorithms
  • Ensemble Learning Assisted Evolutionary Algorithms
  • Generative Learning Assisted Evolutionary Algorithms
  • Model Based Selection for General Evolutionary Optimization
  • Model Based Objective Space Analysis/Reconstruction for Evolutionary Multi-/Many-objective Optimization
  • Model Based Decision Space Analysis/Reconstruction for Evolutionary Large-scale Optimization
  • Model Based Offspring Generation for Evolutionary Dynamic Optimization

Symposium Chairs

Programme Committee

Aiming Zhou, East China Normal University, China
Bo Liu, Wrexham Glyndwr University, UK
Changwu Huang, Southern University of Science and Technology, China
Chaoli Sun, Taiyuan University of Technology, China
Handing Wang, Xidian University, China
Jonathan Fieldsend, University of Exeter, UK
Juergen Branke, University of Warwick, UK
Richard Allmendinger, University of Manchester, UK
Tapabrata Ray, University of New South Wales, Australia
Tinkle Chugh, University of Exeter, UK
Xingyi Zhang, Anhui University, China
Ye Tian, Anhui University, China
Yi Xiang, South China University of Technology, China
Yuren Zhou, Sun Yat-Sen University, China
Zhichao Lu, Southern University of Science and Technology, China