Topics of interest include, but are not limited to:
Run time analysis Mathematical tools suitable for the analysis of search heuristics Fitness landscapes and problem difficulty Configuration and selection of algorithms, heuristics, operators, and parameters Stochastic and dynamic environments, noisy evaluations Constrained optimization Problem representation Complexity theory for search heuristics Multi-objective optimization Benchmarking Connections between black-box optimization and machine learning