2015 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments
- 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
Proposals for special session and tutorials are encouraged. Please send your proposal to one of the co-chairs by May 14, 2015.
De Montfort University, UK.
University of Surrey, UK.
Rowan University, USA.