2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments

Topics of the interest include but are not limited to: 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