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