- 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 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
- Dr. Yaochu Jin, Honda Research Institute Europe, Germany. Email: yaochu.jin . at. honda-ri.de
- Dr. Shengxiang Yang, University of Leicester, UK. Email: s.yang . at . mcs.le.ac.uk
- Dr. Robi Polikar, Rowan University, USA. Email: polikar . at . rowan.edu
- Cesare Alippi, Politecnico di Milano, Italy
- Hans-Georg Beyer, Vorarlberg University of Applied Sciences, Austria (TBC)
- Gavin Brown, University of Manchester, UK
- Tim Blackwell, Goldsmiths College London, UK (TBC)
- Juergen Branke, University of Warwick, UK
- Hui Cheng, University of Leicester, UK
- Ernesto Costa, University of Coimbra, Portugal
- Jing Gao, University of Illinois, Urbano Champaign (TBC)
- Chi-Keong Goh, Rolls-Royce Singapore Pte Ltd, Singapore
- Abdelhamid Bouchachia, University of Klagenfurt, Austria
- Haibo He, University of Rhode Island, USA
- Ivan Koychev, Bulgarian Academy of Sciences, Bulgaria
- Xiaodong Li, RMIT University, Australia (TBC)
- Yan Meng, Stevens Institute of Technology, USA
- Ferrante Neri, University of Jyvaskyla, Finland
- Yew-Soon Ong, Nanyang Technological University, Singapore (TBC)
- Hendrik Richter, University of Leipzig, Germany
- Philipp Rohlfshagen, University of Birmingham, UK
- Kay Chen Tan, National University of Singapore, Singapore (TBC)
- Renato Tinos, Universidade de Sao Paulo, Brazil (TBC)
- Sima Uyar, Istanbul Technical University, Turkey (TBC)
- Hongfeng Wang, Northeastern University, China
- Jeremy Watts, University of Birmingham, UK
- Qingfu Zhang, University of Essex, UK
- Vilnius University, Lithuania
Motivation and Scope
Computational Intelligence (CI) methodologies, including evolutionary algorithms, neural networks and fuzzy systems have shown to be well suited to deal with significant uncertainties that may be encountered in solving real-world problems. The purpose of this symposium is to bring together scientists, engineers, and graduate students to present and discuss recent advances in employing CI for solving scientific and engineering problems in the presence of uncertainties. Topics of the interest include but are not limited to:Evolutionary computation in dynamic and uncertain environments
Learning and adaptation in evolutionary computation
Hybrid methodologies for dealing with uncertainties
Interactions of evolution and learning in changing environments
Benchmarks, performance measures, and real-world applications