A. Auger and B. Doerr, Theory of Randomized Search Heuristics: Foundations and Recent Developments, vol.1, 2011.

S. Böttcher, B. Doerr, and F. Neumann, Optimal Fixed and Adaptive Mutation Rates for the LeadingOnes Problem, Proc. of Parallel Problem Solving from Nature (PPSN'10), vol.6238, pp.1-10, 2010.

, 2012. Variants of Evolutionary Algorithms for Real-World Applications

R. Dang-nhu, T. Dardinier, B. Doerr, G. Izacard, and D. Nogneng, A New Analysis Method for Evolutionary Optimization of Dynamic and Noisy Objective Functions, Proc. of Genetic and Evolutionary Computation Conference (GECCO'18), pp.1467-1474, 2018.

K. Deb, Optimization for Engineering Design -Algorithms and Examples, Second Edition. PHI Learning Private Limited, 2012.

B. Doerr, Better Runtime Guarantees Via Stochastic Domination, Proc. of Evolutionary Computation in Combinatorial Optimization (EvoCOP'18), 2018.

. Springer, , pp.1-17

B. Doerr, Probabilistic Tools for the Analysis of Randomized Optimization Heuristics, 2018.

B. Doerr and M. Gnewuch, Nils Hebbinghaus, and Frank Neumann, Proc. of IEEE Congress on Evolutionary Computation (CEC'07), pp.2591-2597, 2007.

B. Doerr, E. Happ, and C. Klein, Tight Analysis of the (1+1)-EA for the Single Source Shortest Path Problem, Evolutionary Computation, vol.19, pp.673-691, 2011.

B. Doerr, E. Happ, and C. Klein, Crossover Can Provably be Useful in Evolutionary Computation, Theoretical Computer Science, vol.425, pp.17-33, 2012.

B. Doerr, D. Sudholt, and C. Witt, When Do Evolutionary Algorithms Optimize Separable Functions in Parallel, Proc. of Foundations of Genetic Algorithms (FOGA'13), pp.48-59, 2013.

T. Jansen, Analyzing Evolutionary Algorithms -The Computer Science Perspective, 2013.

J. Lässig and D. Sudholt, Design and Analysis of Migration in Parallel Evolutionary Algorithms, Soft Computing, vol.17, pp.1121-1144, 2013.

A. Lissovoi and C. Witt, Runtime Analysis of Ant Colony Optimization on Dynamic Shortest Path Problems, Theoretical Computer Science, vol.561, pp.73-85, 2015.

F. Neumann and I. Wegener, Minimum Spanning Trees Made Easier via Multi-Objective Optimization, Natural Computing, vol.5, pp.305-319, 2006.

F. Neumann and I. Wegener, Randomized local search, evolutionary algorithms, and the minimum spanning tree problem, Theoretical Computer Science, vol.378, pp.32-40, 2007.

F. Neumann and C. Witt, Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity, 2010.

F. Neumann and C. Witt, On the Runtime of Randomized Local Search and Simple Evolutionary Algorithms for Dynamic Makespan Scheduling, Proc. of International Joint Conference on Artificial Intelligence (IJCAI'15), pp.3742-3748, 2015.

T. T. Nguyen and X. Yao, Continuous Dynamic Constrained Optimization: The Challenges, IEEE Transactions on Evolutionary Computation, vol.16, pp.769-786, 2012.

M. Pourhassan, W. Gao, and F. Neumann, Maintaining 2-Approximations for the Dynamic Vertex Cover Problem Using Evolutionary Algorithms, Proc. of Genetic and Evolutionary Computation Conference (GECCO'15), 2015.

, , pp.903-910

R. Günther, G. Raidl, B. A. Koller, and . Julstrom, Biased Mutation Operators for Subgraph-Selection Problems, IEEE Trans. Evolutionary Computation, vol.10, pp.145-156, 2006.

P. Rakshit, A. Konar, and S. Das, Noisy evolutionary optimization algorithms -A comprehensive survey, Swarm and Evolutionary Computation, vol.33, pp.18-45, 2017.

J. Reichel and M. Skutella, On the size of weights in randomized search heuristics, Proc. of Foundations of Genetic Algorithms (FOGA'09), pp.21-28, 2009.

H. Richter and S. Yang, Dynamic Optimization Using Analytic and Evolutionary Approaches: A Comparative Review. In Handbook of Optimization -From Classical to Modern Approach, Intelligent Systems Reference Library, vol.38, pp.1-28, 2013.

V. Roostapour, A. Neumann, and F. Neumann, On the Performance of Baseline Evolutionary Algorithms on the Dynamic Knapsack Problem, Proc. of Parallel Problem Solving from Nature (PPSN'18), vol.11101, pp.158-169, 2018.

V. Roostapour, A. Neumann, F. Neumann, and T. Friedrich, Pareto Optimization for Subset Selection with Dynamic Cost Constraints, 2018.

V. Roostapour, M. Pourhassan, and F. Neumann, Analysis of Evolutionary Algorithms in Dynamic and Stochastic Environments, 2018.

J. E. Rowe and D. Sudholt, The Choice of the Offspring Population Size in the (1, ?) Evolutionary Algorithm, Theoretical Computer Science, vol.545, pp.20-38, 2014.

B. Schieber, H. Shachnai, G. Tamir, and T. Tamir, A Theory and Algorithms for Combinatorial Reoptimization, Algorithmica, vol.80, pp.576-607, 2018.

F. Shi, F. Neumann, and J. Wang, Runtime Analysis of Randomized Search Heuristics for the Dynamic Weighted Vertex Cover Problem, Proc. of the Genetic and Evolutionary Computation Conference (GECCO'18), pp.1515-1522, 2018.

F. Shi, M. Schirneck, T. Friedrich, T. Kötzing, and F. Neumann, Reoptimization Times of Evolutionary Algorithms on Linear Functions Under Dynamic Uniform Constraints, Proc. of the Genetic and Evolutionary Computation Conference (GECCO'17), pp.1407-1414, 2017.

D. Sudholt, On the Robustness of Evolutionary Algorithms to Noise: Refined Results and an Example where Noise Helps, Proc. of the Genetic and Evolutionary Computation Conference (GECCO'18), pp.1523-1530, 2018.

I. Wegener, Theoretical Aspects of Evolutionary Algorithms, Proc. of Automata, Languages and Programming (ICALP'01), vol.2076, pp.64-78, 2001.

C. Witt, Revised analysis of the (1+1) EA for the minimum spanning tree problem, Proc. of Genetic and Evolutionary Computation Conference (GECCO'14), pp.509-516, 2014.

A. Zych-pawlewicz, Reoptimization of NP-Hard Problems, Adventures Between Lower Bounds and Higher Altitudes -Essays Dedicated to Juraj Hromkovi? on the Occasion of His 60th Birthday, vol.11011, pp.477-494, 2018.