### Journal articles

- Oliveto PS, Paixão T, Pérez Heredia J, Sudholt D & Trubenová B (2017) How to Escape Local Optima in Black Box Optimisation: When Non-elitism Outperforms Elitism. Algorithmica. View this article in WRRO
- Corus D, He J, Jansen T, Oliveto PS, Sudholt D & Zarges C (2017) On Easiest Functions for Mutation Operators in Bio-Inspired Optimisation. Algorithmica, 78(2), 714-740. View this article in WRRO
- Perez Heredia J, Trubenova B, Sudholt D & Paixao T (2017) Selection limits to adaptive walks on correlated landscapes. Genetics, 205(2), 803-825. View this article in WRRO
- Moraglio A & Sudholt D (2017) Principled Design and Runtime Analysis of Abstract Convex Evolutionary Search. Evolutionary Computation, 25(2), 205-236. View this article in WRRO
- Nallaperuma S, Neumann F & Sudholt D (2016) Expected Fitness Gains of Randomized Search Heuristics for the Traveling Salesperson Problem. Evolutionary Computation, 25(4), 673-705. View this article in WRRO
- Paixao T, Perez Heredia J, Sudholt D & Trubenova B (2016) Towards a Runtime Comparison of Natural and Artificial Evolution. Algorithmica. View this article in WRRO
- Kempka J, McMinn P & Sudholt D (2015) Design and analysis of different alternating variable searches for search-based software testing. Theoretical Computer Science, 605, 1-20. View this article in WRRO
- Paixão T, Badkobeh G, Barton N, Çörüş D, Dang D-C, Friedrich T, Lehre PK, Sudholt D, Sutton AM & Trubenová B (2015) Toward a unifying framework for evolutionary processes. Journal of Theoretical Biology, 383, 28-43. View this article in WRRO
- Sudholt D (2014) How Crossover Speeds Up Building-Block Assembly in Genetic Algorithms. View this article in WRRO
- Lässig J & Sudholt D (2014) General Upper Bounds on the Runtime of Parallel Evolutionary Algorithms. Evolutionary Computation, 22(3), 405-437. View this article in WRRO
- Lässig J & Sudholt D (2014) Analysis of Speedups in Parallel Evolutionary Algorithms and (1+lambda) EAs for Combinatorial Optimization. Theoretical Computer Science.
- Minku LL, Sudholt D & Yao X (2014) Improved evolutionary algorithm design for the project scheduling problem based on runtime analysis. IEEE Transactions on Software Engineering, 40(1), 83-102.
- Doerr B, Jansen T, Sudholt D, Winzen C & Zarges C (2013) Mutation rate matters even when optimizing monotonic functions. Evolutionary Computation, 21(1), 1-27.
- Rowe JE & Sudholt D (2013) The choice of the offspring population size in the (1, λ) evolutionary algorithm. Theoretical Computer Science.
- Doerr B, Sudholt D & Witt C (2013) When do evolutionary algorithms optimize separable functions in parallel?. FOGA 2013 - Proceedings of the 12th ACM Workshop on Foundations of Genetic Algorithms, 51-63.
- Lässig J & Sudholt D (2013) Design and analysis of migration in parallel evolutionary algorithms. Soft Computing, 17(7), 1121-1144.
- Sudholt D & Thyssen C (2012) Running time analysis of ant colony optimization for shortest path problems. Journal of Discrete Algorithms, 10(1), 165-180.
- Sudholt D & Thyssen C (2011) A Simple Ant Colony Optimizer for Stochastic Shortest Path Problems. Algorithmica (New York), 1-30.
- Sudholt D (2011) A New Method for Lower Bounds on the Running Time of Evolutionary Algorithms. CoRR, abs/1109.1504.
- Doerr B, Neumann F, Sudholt D & Witt C (2011) Runtime analysis of the 1-ANT ant colony optimizer. Theoretical Computer Science, 412(17), 1629-1644.
- Sudholt D & Thyssen C (2011) Running time analysis of Ant Colony Optimization for shortest path problems. Journal of Discrete Algorithms.
- Sudholt D & Witt C (2010) Runtime analysis of a binary particle swarm optimizer. THEORETICAL COMPUTER SCIENCE, 411(21), 2084-2100.
- Jansen T & Sudholt D (2010) Analysis of an Asymmetric Mutation Operator. EVOLUTIONARY COMPUTATION, 18(1), 1-26.
- Sauerwald T & Sudholt D (2010) A self-stabilizing algorithm for cut problems in synchronous networks. Theoretical Computer Science, 411(14-15), 1599-1612.
- Neumann F, Sudholt D & Witt C (2009) Analysis of different MMAS ACO algorithms on unimodal functions and plateaus. Swarm Intelligence, 3(1), 35-68.
- Sudholt D (2009) The impact of parametrization in memetic evolutionary algorithms. Theoretical Computer Science, 410(26), 2511-2528.
- Friedrich T, Oliveto PS, Sudholt D & Witt C (2009) Analysis of diversity-preserving mechanisms for global exploration.. Evol Comput, 17(4), 455-476.
- Mambrini A & Sudholt D () Design and Analysis of Schemes for Adapting Migration Intervals in Parallel Evolutionary Algorithms. Evolutionary Computation. View this article in WRRO

### Chapters

- Sudholt D (2015) Parallel Evolutionary Algorithms In Kacprzyk J & Pedrycz W (Ed.), Handbook of Computational Intelligence (pp. 929-959). Springer
- Sudholt D (2012) Parametrization and balancing local and global search (pp. 55-72).
- Sudholt D (2011) Memetic Evolutionary Algorithms In Auger A & Doerr B (Ed.), Theory of Randomized Search Heuristics (pp. 141-169). World Scientific Publishing Company
- Neumann F, Sudholt D & Witt C (2009) Computational complexity of ant colony optimization and its hybridization with local search (pp. 91-120).

### Conference proceedings papers

- Lissovoi A, Sudholt D, Wagner M & Zarges C (2017) Theoretical results on bet-and-run as an initialisation strategy. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 17) (pp 857-864), 15 July 2017 - 19 July 2017. View this article in WRRO
- Covantes Osuna E, Gao W, Neumann F & Sudholt D (2017) Speeding Up Evolutionary Multi-objective Optimisation Through Diversity-Based Parent Selection. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 17) (pp 553-560), 15 July 2017 - 19 July 2017. View this article in WRRO
- Nallaperuma S, Oliveto PS, Perez Heredia J & Sudholt D (2017) When is it Beneficial to Reject Improvements?. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 17) (pp 1391-1398), 15 July 2017 - 19 July 2017. View this article in WRRO
- Covantes Osuna E & Sudholt D (2017) Analysis of the Clearing Diversity-Preserving Mechanism. 14th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA '17), 12 January 2017 - 15 January 2017. View this article in WRRO
- Dang D, Friedrich T, Kötzing T, Krejca MS, Lehre PK, Oliveto PS, Sudholt D & Sutton AM (2016) Emergence of Diversity and its Benefits for Crossover in Genetic Algorithms. Parallel Problem Solving from Nature – PPSN XIV View this article in WRRO
- Oliveto PS, Paixao T, Perez Heredia J, Sudholt D & Trubenova B (2016) When Non-Elitism Outperforms Elitism for Crossing Fitness Valleys. GECCO '16 Proceedings of the Genetic and Evolutionary Computation Conference 2016 (pp 1163-1170), 20 July 2016 - 24 July 2016. View this article in WRRO
- Badkobeh G, Lehre PK & Sudholt D (2015) Black-box Complexity of Parallel Search with Distributed Populations. Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII (pp 3-15)
- Mambrini A & Sudholt D (2014) Design and analysis of adaptive migration intervals in parallel evolutionary algorithms. Genetic and Evolutionary Computation Conference (GECCO 2014) (pp 1047-1054). Vancouver, BC, Canada, 12 July 2014 - 16 July 2014.
- Oliveto PS & Sudholt D (2014) On the runtime analysis of stochastic ageing mechanisms. Genetic and Evolutionary Computation Conference (GECCO 2014) (pp 113-120). Vancouver, BC, Canada, 12 July 2014 - 16 July 2014.
- Nallaperuma S, Neumann F & Sudholt D (2014) A fixed budget analysis of randomized search heuristics for the traveling salesperson problem. Genetic and Evolutionary Computation Conference (GECCO 2014) (pp 807-814). Vancouver, BC, Canada, 12 July 2014 - 17 July 2014.
- Oliveto PS, Sudholt D & Zarges C (2014) On the Runtime Analysis of Fitness Sharing Mechanisms. Parallel Problem Solving from Nature – PPSN XIII (pp 932-941)
- Badkobeh G, Lehre PK & Sudholt D (2014) Unbiased Black-Box Complexity of Parallel Search. Parallel Problem Solving from Nature – PPSN XIII (pp 892-901)
- Kempka J, McMinn P & Sudholt D (2013) A theoretical runtime and empirical analysis of different alternating variable searches for search-based testing. Genetic and Evolutionary Computation Conference (GECCO 2013) (pp 1445-1452). Amsterdam, 6 July 2013 - 10 July 2013.
- Mambrini A, Sudholt D & Yao X (2012) Homogeneous and heterogeneous island models for the set cover problem. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 7491 LNCS(PART 1) (pp 11-20)
- Sudholt D (2012) Crossover speeds up building-block assembly. GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation (pp 689-696)
- Moraglio A & Sudholt D (2012) Runtime analysis of convex evolutionary search. GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation (pp 649-656)
- Rowe JE & Sudholt D (2012) The choice of the offspring population size in the (1,λ) EA. GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation (pp 1349-1356)
- Minku LL, Sudholt D & Yao X (2012) Evolutionary algorithms for the project scheduling problem: Runtime analysis and improved design. GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation (pp 1221-1228)
- Neumann F, Oliveto PS, Rudolph G & Sudholt D (2011) On the effectiveness of crossover for migration in parallel evolutionary algorithms. Genetic and Evolutionary Computation Conference, GECCO'11 (pp 1587-1594)
- Kötzing T, Sudholt D & Theile M (2011) How crossover helps in Pseudo-Boolean optimization. Genetic and Evolutionary Computation Conference, GECCO'11 (pp 989-996)
- Lässig J & Sudholt D (2011) Analysis of speedups in parallel evolutionary algorithms for combinatorial optimization (Extended abstract). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 7074 LNCS (pp 405-414)
- Lässig J & Sudholt D (2011) Adaptive population models for offspring populations and parallel evolutionary algorithms. FOGA'11 - Proceedings of the 2011 ACM/SIGEVO Foundations of Genetic Algorithms XI (pp 181-192)
- Sudholt D (2011) Using Markov-chain mixing time estimates for the analysis of ant colony optimization. FOGA'11 - Proceedings of the 2011 ACM/SIGEVO Foundations of Genetic Algorithms XI (pp 139-150)
- Kötzing T, Neumann F, Sudholt D & Wagner M (2011) Simple max-min ant systems and the optimization of linear pseudo-Boolean functions. FOGA'11 - Proceedings of the 2011 ACM/SIGEVO Foundations of Genetic Algorithms XI (pp 209-218)
- Sudholt D (2010) General Lower Bounds for the Running Time of Evolutionary Algorithms. PARALLEL PROBLEMS SOLVING FROM NATURE - PPSN XI, PT I, Vol. 6238 (pp 124-133)
- Neumann F, Sudholt D & Witt C (2010) A few ants are enough: ACO with iteration-best update.. GECCO (pp 63-70)
- Lässig J & Sudholt D (2010) The benefit of migration in parallel evolutionary algorithms.. GECCO (pp 1105-1112)
- Laessig J & Sudholt D (2010) Experimental Supplements to the Theoretical Analysis of Migration in the Island Model. PARALLEL PROBLEMS SOLVING FROM NATURE - PPSN XI, PT I, Vol. 6238 (pp 224-233)
- Laessig J & Sudholt D (2010) General Scheme for Analyzing Running Times of Parallel Evolutionary Algorithms. PARALLEL PROBLEMS SOLVING FROM NATURE - PPSN XI, PT I, Vol. 6238 (pp 234-243)
- Horoba C & Sudholt D (2010) Ant Colony Optimization for stochastic shortest path problems. Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 (pp 1465-1472)
- Doerr B, Jansen T, Sudholt D, Winzen C & Zarges C (2010) Optimizing monotone functions can be difficult. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 6238 LNCS(PART 1) (pp 42-51)
- Sudholt D & Zarges C (2010) Analysis of an iterated local search algorithm for vertex coloring. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 6506 LNCS(PART 1) (pp 340-352)
- Horoba C & Sudholt D (2009) Running time analysis of ACO systems for shortest path problems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 5752 LNCS (pp 76-91)
- Sauerwald T & Sudholt D (2008) Self-stabilizing cuts in synchronous networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 5058 LNCS (pp 234-246)
- Neumann F, Sudholt D & Witt C (2008) Rigorous analyses for the combination of ant colony optimization and local search. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 5217 LNCS (pp 132-143)
- Sudholt D & Witt C (2008) Runtime analysis of binary PSO. GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008 (pp 135-142)
- Sudholt D (2008) Memetic algorithms with variable-depth search to overcome local optima. GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008 (pp 787-794)
- Friedrich T, Oliveto PS, Sudholt D & Witt C (2008) Theoretical analysis of diversity mechanisms for global exploration. GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008 (pp 945-952)
- Doerr B, Neumann F, Sudholt D & Witt C (2007) On the runtime analysis of the 1-ANT ACO algorithm. Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference (pp 33-40)
- Neumann F, Sudholt D & Witt C (2007) Comparing variants of MMAS ACO algorithms on pseudo-boolean functions. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 4638 LNCS (pp 61-75)
- Sudholt D (2006) On the analysis of the (1+1) memetic algorithm. GECCO 2006: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOL 1 AND 2 (pp 493-500)
- Sudholt D (2006) Local search in evolutionary algorithms: The impact of the local search frequency. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 4288 LNCS (pp 359-368)
- Jansen T & Sudholt D (2005) Design and analysis of an asymmetric mutation operator. 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings, Vol. 1 (pp 190-197)
- Sudholt D (2005) Crossover is probably essential for the ising model on trees. GECCO 2005 - Genetic and Evolutionary Computation Conference (pp 1161-1167)
- Briest P, Brockhoff D, Degener B, Englert M, Gunia C, Heering O, Jansen T, Leifhelm M, Plociennik K, Röglin H, Schweer A, Sudholt D, Tannenbaum S & Wegener I (2004) The ising model: Simple evolutionary algorithms as adaptation schemes. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 3242 (pp 31-40)
- Briest P, Brockhoff D, Degener B, Englert M, Gunia C, Heering O, Jansen T, Leifhelm M, Plociennik K, Röglin H, Schweer A, Sudholt D, Tannenbaum S & Wegener I (2004) Experimental supplements to the theoretical analysis of EAs on problems from combinatorial optimization. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 3242 (pp 21-30)
- Sudholt D, Oliveto P, Lehre PK, Dang D, Friedrich T, Kötzing T, Krejca M & Sutton AM () Escaping Local Optima with Diversity Mechanisms and Crossover. Genetic and Evolutionary Computation Conference (GECCO 2016)
- Sudholt D & Goldman B () Runtime Analysis for the Parameter-less Population Pyramid. Genetic and Evolutionary Computation Conference (GECCO 2016)
- Sudholt D & Witt C () Update Strength in EDAs and ACO: How to Avoid Genetic Drift. Genetic and Evolutionary Computation Conference (GECCO 2016)
- Paixao T, Perez Heredia J, Sudholt D & Trubenova B () First Steps Towards a Runtime Comparison of Natural and Artificial Evolution. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2015)
- Corus D, He J, Jansen T, Oliveto PS, Sudholt D & Zarges C () On Easiest Functions for Somatic Contiguous Hypermutations And Standard Bit Mutations. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2015)