1 Introduction  
2 Fine-grained Runtime Analysis of Randomized Search Heuristics  
2.1 Fixed Budget and Fixed Target Analysis  
2.2 Parameterized Analysis  
2.3 Future Work  
3 Setting the Parameters of Evolutionary Algorithms  
3.1 Finding Optimal Static Parameter Values (Parameter Tuning)  
3.2 Dynamic Parameter Settings (Parameter Control)  
3.3 The Future of Parameter Research  
4 Analysis of Evolutionary Algorithms in Dynamic and Stochastic Environments  
4.1 Dynamic Benchmark Functions  
4.2 Dynamic Combinatorial Optimization Problems  
4.3 Noisy Problems  
4.4 Combinatorial Optimization Problems with Dynamic and Stochastic Constraints  
4.5 Future Work  
5 Submodular Optimization  
5.1 Monotone Submodular Functions  
5.2 Non-monotone Submodular Functions  
5.3 Submodular Functions with Dynamic and Stochastic Constraints  
5.4 Future Work  
6 Theory of Estimation-of-Distribution Algorithms  
6.1 The Compact Genetic Algorithm  
6.2 Central Results  
6.3 Open Problems  
7 Drift Analysis  
7.1 Three True Drift Theorems  
7.2 Drift Results With Additional Requirements  
7.3 Challenge: Finding the Right Potential Function  
8 Final Words