Dr Dirk Sudholt

PhD

School of Computer Science

Visiting Professor

d.sudholt@sheffield.ac.uk

Full contact details

Dr Dirk Sudholt
School of Computer Science

Profile

Until September 2020 I was a Senior Lecturer at the University of Sheffield in the Department of Computer Science, heading the newly established Algorithms research group. Before coming to Sheffield, I obtained my Diploma and my Ph.D. from the Technische Universität Dortmund under the supervision of Prof. Ingo Wegener.

I have held postdoc positions at the International Computer Science Institute (ICSI) in Berkeley, California, in the group of Prof. Richard M. Karp as well as the University of Birmingham, working with Prof. Xin Yao in the SEBASE project.

Research interests

I am interested in randomised algorithms, algorithmic analysis, and combinatorial optimisation. My main expertise is the analysis of bio-inspired search heuristics such as evolutionary algorithms, ant colony optimisation, particle swarm optimisation as well as hybrid and parallel variants thereof.

I am interested in rigorous analyses of their optimisation time: the expected time until a search heuristic finds a satisfactory solution for an interesting problem.Such studies give insight into the working principles of bio-inspired search heuristics.

They tell us how effective these metaheuristics are in comparison to problem-specific algorithms and how design choices such as the choice of operators and parameters affect performance. This helps practitioners to make informed design choices and contributes to a rigorous theoretical foundation of metaheuristics.

Publications

Journal articles

Nguyen PTH & Sudholt D (2020) Memetic algorithms outperform evolutionary algorithms in multimodal optimisation. Artificial Intelligence, 287. View this article in WRRO
Sudholt D (2020) Analysing the robustness of evolutionary algorithms to noise : refined runtime bounds and an example where noise is beneficial. Algorithmica. View this article in WRRO
Lehre PK & Sudholt D (2019) Parallel black-box complexity with tail bounds. IEEE Transactions on Evolutionary Computation. View this article in WRRO
Oliveto PS, Sudholt D & Zarges C (2019) On the benefits and risks of using fitness sharing for multimodal optimisation. Theoretical Computer Science, 773, 53-70. View this article in WRRO
Covantes Osuna E & Sudholt D (2019) Runtime Analysis of Crowding Mechanisms for Multimodal Optimisation. IEEE Transactions on Evolutionary Computation. View this article in WRRO
Sudholt D & Witt C (2019) On the Choice of the Update Strength in Estimation-of-Distribution Algorithms and Ant Colony Optimization. Algorithmica, 81(4), 1450-1489. View this article in WRRO
Nallaperuma S, Oliveto PS, Perez Heredia J & Sudholt D (2019) On the Analysis of Trajectory-Based Search Algorithms: When is it Beneficial to Reject Improvements?. Algorithmica, 81(2), 858-885. View this article in WRRO
Doerr C & Sudholt D (2019) Preface to the Special Issue on Theory of Genetic and Evolutionary Computation. Algorithmica, 81(2), 589-592.
Covantes Osuna E, Gao W, Neumann F & Sudholt D (2018) Design and Analysis of Diversity-Based Parent Selection Schemes for Speeding Up Evolutionary Multi-objective Optimisation. Theoretical Computer Science. View this article in WRRO
Covantes Osuna E & Sudholt D (2018) On the Runtime Analysis of the Clearing Diversity-Preserving Mechanism. Evolutionary Computation. View this article in WRRO
Kötzing T & Sudholt D (2017) Preface to the Special Issue on Theory of Genetic and Evolutionary Computation. Algorithmica, 1-4.
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
Dang D-C, Friedrich T, Kötzing T, Krejca M, Lehre PK, Oliveto PS, Sudholt D & Sutton AM (2017) Escaping Local Optima Using Crossover with Emergent Diversity. IEEE Transactions on Evolutionary Computation, 22(3), 484-497. View this article in WRRO
Paixao T, Perez Heredia J, Sudholt D & Trubenova B (2017) Towards a Runtime Comparison of Natural and Artificial Evolution. Algorithmica, 78(2), 681-713. 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
Sudholt D (2017) How Crossover Speeds up Building Block Assembly in Genetic Algorithms. Evolutionary Computation, 25(2), 237-274. 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
Mambrini A & Sudholt D (2015) Design and Analysis of Schemes for Adapting Migration Intervals in Parallel Evolutionary Algorithms. Evolutionary Computation, 23(4), 559-582. 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
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
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 (2011) Hybridizing evolutionary algorithms with variable-depth search to overcome local optima. Algorithmica (New York), 59(3), 343-368.
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.

Chapters

Sudholt D (2020) The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses, Natural Computing Series (pp. 359-404). Springer International Publishing
Sudholt D (2015) Parallel Evolutionary Algorithms In Kacprzyk J & Pedrycz W (Ed.), Springer Handbook of Computational Intelligence (pp. 929-959). Berlin: 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

Hall GT, Oliveto PS & Sudholt D (2020) Analysis of the performance of algorithm configurators for search heuristics with global mutation operators. Proceedings of the 2020 Genetic and Evolutionary Computation Conference
Oliveto PS, Sudholt D & Witt C (2020) A tight lower bound on the expected runtime of standard steady state genetic algorithms. Proceedings of the 2020 Genetic and Evolutionary Computation Conference View this article in WRRO
Foster M, Hughes M, O'Brien G, Oliveto PS, Pyle J, Sudholt D & Williams J (2020) Do sophisticated evolutionary algorithms perform better than simple ones?. Proceedings of the 2020 Genetic and Evolutionary Computation Conference View this article in WRRO
Albunian N, Fraser G & Sudholt D (2020) Causes and effects of fitness landscapes in unit test generation. Proceedings of the 2020 Genetic and Evolutionary Computation Conference View this article in WRRO
Fajardo MAH & Sudholt D (2020) On the choice of the parameter control mechanism in the (1+( λ, λ )) genetic algorithm. Proceedings of the 2020 Genetic and Evolutionary Computation Conference View this article in WRRO
Bossek J, Neumann F, Peng P & Sudholt D (2020) More effective randomized search heuristics for graph coloring through dynamic optimization. Proceedings of the 2020 Genetic and Evolutionary Computation Conference View this article in WRRO
Bossek J, Neumann F, Peng P & Sudholt D (2019) Runtime analysis of randomized search heuristics for dynamic graph coloring. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 19). Prague, Czech Republic, 13 July 2019 - 17 July 2019. View this article in WRRO
Hall GT, Oliveto P & Sudholt D (2019) On the impact of the cutoff time on the performance of algorithm configurators. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2019). Prague, Czech Republic, 13 July 2019 - 17 July 2019. View this article in WRRO
Bossek J & Sudholt D (2019) Time complexity analysis of RLS and (1 + 1) EA for the edge coloring problem. Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms - FOGA '19, 27 August 2019 - 29 August 2019. View this article in WRRO
Covantes Osuna E & Sudholt D (2018) Empirical analysis of diversity-preserving mechanisms on example landscapes for multimodal optimisation. Parallel Problem Solving from Nature – PPSN XV, Vol. 11102 LNCS (pp 207-219), 8 September 2018 - 12 September 2018. View this article in WRRO
Nguyen PTH & Sudholt D (2018) Memetic Algorithms Beat Evolutionary Algorithms on the Class of Hurdle Problems. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2018) View this article in WRRO
Lengler J, Sudholt D & Witt C (2018) Medium Step Sizes are Harmful for the Compact Genetic Algorithm. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2018) (pp 1499-1506), 15 July 2018 - 19 July 2018. View this article in WRRO
Sudholt D (2018) On the Robustness of Evolutionary Algorithms to Noise: Refined Results and an Example Where Noise Helps. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2018) View this article in WRRO
Covantes Osuna E & Sudholt D (2018) Runtime Analysis of Probabilistic Crowding and Restricted Tournament Selection for Bimodal Optimisation. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2018) View this article in WRRO
Sudholt D (2017) Theory of swarm intelligence. Proceedings of the Genetic and Evolutionary Computation Conference Companion
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
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
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
Igel C, Sudholt D & Witt C (2017) FOGA 2017 chairs' welcome. FOGA 2017 - Proceedings of the 14th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (pp iii)
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
Sudholt D (2016) Theory of Swarm Intelligence. Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion - GECCO '16 Companion, 20 July 2016 - 24 July 2016.
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
Dang D-C, Friedrich T, Kötzing T, Krejca MS, Lehre PK, Oliveto PS, Sudholt D & Sutton AM (2016) Escaping Local Optima with Diversity Mechanisms and Crossover. Proceedings of the 2016 on Genetic and Evolutionary Computation Conference - GECCO '16, 20 July 2016 - 24 July 2016.
Sudholt D & Witt C (2016) Update Strength in EDAs and ACO. Proceedings of the 2016 on Genetic and Evolutionary Computation Conference - GECCO '16, 20 July 2016 - 24 July 2016.
Goldman BW & Sudholt D (2016) Runtime Analysis for the Parameter-less Population Pyramid. Proceedings of the 2016 on Genetic and Evolutionary Computation Conference - GECCO '16, 20 July 2016 - 24 July 2016.
Doerr C, Bredeche N, Alba E, Bartz-Beielstein T, Brockhoff D, Doerr B, Eiben G, Epitropakis MG, Fonseca CM, Guerreiro A , Haasdijk E et al (2016) Tutorials at PPSN 2016 (pp 1012-1022)
Paixao T, Pérez Heredia J, Sudholt D & Trubenova B (2015) First Steps Towards a Runtime Comparison of Natural and Artificial Evolution. Proceedings of the 2015 on Genetic and Evolutionary Computation Conference - GECCO '15, 11 July 2015 - 15 July 2015.
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 - FOGA '15, 17 January 2015 - 22 January 2015.
Corus D, He J, Jansen T, Oliveto PS, Sudholt D & Zarges C (2015) On Easiest Functions for Somatic Contiguous Hypermutations And Standard Bit Mutations. Proceedings of the 2015 on Genetic and Evolutionary Computation Conference - GECCO '15, 11 July 2015 - 15 July 2015.
Nallaperuma S, Neumann F & Sudholt D (2014) A fixed budget analysis of randomized search heuristics for the traveling salesperson problem. Proceedings of the 2014 conference on Genetic and evolutionary computation - GECCO '14, 12 July 2014 - 16 July 2014.
Oliveto PS & Sudholt D (2014) On the runtime analysis of stochastic ageing mechanisms. Proceedings of the 2014 conference on Genetic and evolutionary computation - GECCO '14, 12 July 2014 - 16 July 2014.
Mambrini A & Sudholt D (2014) Design and analysis of adaptive migration intervals in parallel evolutionary algorithms. Proceedings of the 2014 conference on Genetic and evolutionary computation - GECCO '14, 12 July 2014 - 16 July 2014.
Sudholt D (2014) Theory of swarm intelligence. Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion - GECCO Comp '14, 12 July 2014 - 16 July 2014.
Badkobeh G, Lehre PK & Sudholt D (2014) Unbiased Black-Box Complexity of Parallel Search (pp 892-901)
Oliveto PS, Sudholt D & Zarges C (2014) On the Runtime Analysis of Fitness Sharing Mechanisms (pp 932-941)
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)
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)
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)
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)
Lässig J & Sudholt D (2012) General Upper Bounds on the Running Time of Parallel Evolutionary Algorithms. CoRR, Vol. abs/1206.3522
Lässig J & Sudholt D (2011) Analysis of Speedups in Parallel Evolutionary Algorithms for Combinatorial Optimization - (Extended Abstract).. ISAAC, Vol. 7074 (pp 405-414)
Kötzing T, Sudholt D & Theile M (2011) How crossover helps in Pseudo-Boolean optimization. Genetic and Evolutionary Computation Conference, GECCO'11 (pp 989-996)
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)
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)
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)
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)
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)
Kötzing T, Neumann F, Sudholt D & Wagner M (2010) Simple Max-Min Ant Systems and the Optimization of Linear Pseudo-Boolean Functions. CoRR, Vol. abs/1007.4707
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)
Doerr B, Jansen T, Sudholt D, Winzen C & Zarges C (2010) Optimizing Monotone Functions Can Be Difficult.. PPSN (1), Vol. 6238 (pp 42-51)
Sudholt D (2010) General Lower Bounds for the Running Time of Evolutionary Algorithms.. PPSN (1), Vol. 6238 (pp 124-133)
Lässig J & Sudholt D (2010) The benefit of migration in parallel evolutionary algorithms.. GECCO (pp 1105-1112)
Neumann F, Sudholt D & Witt C (2010) A few ants are enough: ACO with iteration-best update.. GECCO (pp 63-70)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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 et al (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)
Briest P, Brockhoff D, Degener B, Englert M, Gunia C, Heering O, Jansen T, Leifhelm M, Plociennik K, Röglin H , Schweer A et al (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)
Hall GT, Oliveto PS & Sudholt D () Fast Perturbative Algorithm Configurators. Parallel Problem Solving from Nature View this article in WRRO

Reports

Lässig J & Sudholt D (2011) Adaptive Population Models for Offspring Populations and Parallel Evolutionary Algorithms
Sudholt D (2011) Memetic Algorithms: Parametrization and Balancing Local and Global Search

Theses / Dissertations

Sudholt D (2008) Computational Complexity of Evolutionary Algorithms, Hybridizations, and Swarm Intelligence.

Other

Sudholt D (2012) Theory of swarm intelligence.. GECCO (Companion), 1215-1238.
Sudholt D (2011) Theory of swarm intelligence. Genetic and Evolutionary Computation Conference, GECCO'11 - Companion Publication, 1381-1410.

Preprints

Hall GT, Oliveto PS & Sudholt D (2020) Analysis of the Performance of Algorithm Configurators for Search Heuristics with Global Mutation Operators, arXiv.

Grants: SAGE: Speed of Adaptation in Population Genetics and Evolutionary, EUROPEAN COMMISSION - FP6/FP7, 01/2014 to 12/2016, £262,874, as PI