IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (IEEE CIDUE)

IEEE CIDUE 2021 aims to bring together all researchers, practitioners and students to present and discuss the latest advances in the field of Computational Intelligence (CI), such as neural networks and learning algorithms, fuzzy systems, evolutionary computation and other emerging techniques for dealing with uncertainties encountered in evolutionary optimization, machine learning and data mining.


  • Evolutionary computation in dynamic and uncertain environments
  • Use of surrogates for single and multi-objective optimization
  • Search for robust solutions over space and time
  • Dynamic single and multi-objective optimization
  • Handling noisy fitness functions
  • Learning and adaptation in evolutionary computation
  • Learning in non-stationary and uncertain environments
  • Incremental and lifelong learning
  • Online and interactive learning
  • Dealing with catastrophic forgetting
  • Active and autonomous learning in changing environments
  • Ensemble techniques
  • Multi-objective learning
  • Learning from severely unbalanced data, including multiclass unbalanced data.
  • Mining of temporal patterns
  • Temporal data mining techniques and methodologies
  • Incorporating domain knowledge for efficient temporal data mining
  • Scalability of temporal data mining algorithms
  • Mining of temporal data on the web
  • Hybrid methodologies for dealing with uncertainties, interactions of evolution and learning in changing environments, benchmarks, performance measures, and real-world applications

Symposium Chairs

Michalis Mavrovouniotis (, University of Cyprus)

Changhe Li (, China University of Geosciences)

Shengxiang Yang (, De Montfort University)

Programme Committee

Branke, Juergen
Chong, Siang-Yew
Ding, Jinliang
Helbig, Marde
Li, Bin
Li, Xiaodong
Liang, Jing
Nguyen, Trung Thanh
Ong, Yew-Soon
Pelta, David
Peng, Xingguang
Sun, Chaoli
Ting, Chuan-Kang
Uyar, Sima
Wang, Hongfeng
Yang, Ming
Zhan, Zhi-Hui
Guo, Yi-nan
Yazdani, Danial
Tino, Renato
Jiang, Shouyong
Caraffini, Fabio
Luo, Wenjian