Dr. Dirk Sudholt

I am 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 Diplom 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.
I am interested in randomized algorithms, algorithmic analysis, and combinatorial optimization. My main expertise is the analysis of randomized search heuristics such as evolutionary algorithms, hybridizations with local search, and ant colony optimization.

Contact Information

Address: Department of Computer Science
The University of Sheffield
Regent Court, 211 Portobello
Sheffield S1 4DP
United Kingdom
E-mail: d.sudholtsheffield.ac.uk
Phone: +44 114 222 1848
Office hours: By appointment, Regent Court West, room CG005
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Teaching

Team

Former team members

Admin

Recent Activities

Publications

    Book Chapters

  1. Dirk Sudholt (2018): The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses.
    In Benjamin Doerr and Frank Neumann (Eds.): Theory of Randomized Search Heuristics in Discrete Search Spaces, to be published by Springer.
    Download preprint
  2. Dirk Sudholt (2015): Parallel Evolutionary Algorithms.
    In Janusz Kacprzyk and Witold Pedrycz (Eds.): Handbook of Computational Intelligence, Springer.
    Download preprint
  3. Dirk Sudholt (2012): Parametrization and Balancing Global and Local Search.
    In F. Neri, C. Cotta, and P. Moscato (Eds.): Handbook of Memetic Algorithms, Studies in Computational Intelligence, Vol. 379, ISBN 978-3-642-23246-6, Springer.
    Download preprint
  4. Dirk Sudholt (2011): Memetic Evolutionary Algorithms.
    In A. Auger, B. Doerr (Eds.): Theory of Randomized Search Heuristics - Foundations and Recent Developments, Series on Theoretical Computer Science 1, ISBN: 978-981-4282-66-6, World Scientific.
  5. Frank Neumann, Dirk Sudholt, and Carsten Witt (2009): Computational Complexity of Ant Colony Optimization and its Hybridization with Local Search.
    L.C. Jain, S. Dehuri, CP Lim (Eds.): Innovations in Swarm Intelligence, SGI 248, ISBN: 978-3-642-04224-9, Springer.
    Download preliminary version
  6. Journal Articles

  7. Pietro S. Oliveto, Dirk Sudholt, and Christine Zarges (2018+):
    On the Benefits and Risks of Using Fitness Sharing for Multimodal Optimisation
    To appear in Theoretical Computer Science.
  8. Dirk Sudholt and Carsten Witt (2018+):
    On the Choice of the Update Strength in Estimation-of-Distribution Algorithms and Ant Colony Optimization
    To appear in Algorithmica.
  9. Edgar Covantes Osuna, Wanru Gao, Frank Neumann, and Dirk Sudholt (2018+):
    Design and Analysis of Diversity-Based Parent Selection Schemes for Speeding Up Evolutionary Multi-objective Optimisation
    To appear in Theoretical Computer Science.
  10. Samadhi Nallaperuma, Pietro S. Oliveto, Jorge Pérez Heredia, and Dirk Sudholt (2018+):
    On the Analysis of Trajectory-Based Search Algorithms: When is it Beneficial to Reject Improvements?
    To appear in Algorithmica.
  11. Edgar Covantes Osuna and Dirk Sudholt (2018+):
    On the Runtime Analysis of the Clearing Diversity-Preserving Mechanism
    To appear in Evolutionary Computation.
    Download preprint
  12. Pietro S. Oliveto, Tiago Paixão, Jorge Pérez Heredia, Dirk Sudholt, and Barbora Trubenová (2018):
    How to Escape Local Optima in Black Box Optimisation: When Non-elitism Outperforms Elitism
    Algorithmica 80(5), pages 1604–1633.
  13. Duc-Cuong Dang, Tobias Friedrich, Timo Kötzing, Martin S. Krejca, Per Kristian Lehre, Pietro S. Oliveto, Dirk Sudholt, and Andrew M. Sutton (2018):
    Escaping Local Optima Using Crossover with Emergent Diversity
    IEEE Transactions on Evolutionary Computation 22(3), pages 484-497.
  14. Samadhi Nallaperuma, Frank Neumann, and Dirk Sudholt (2017):
    Expected Fitness Gains of Randomized Search Heuristics for the Traveling Salesperson Problem
    Evolutionary Computation 25(4), pages 673-705.
  15. Jorge Pérez Heredia, Barbora Trubenová, Dirk Sudholt, and Tiago Paixão (2017):
    Selection Limits to Adaptive Walks on Correlated Landscapes
    Genetics, 205(2), pages 803-825.
  16. Tiago Paixão, Jorge Pérez Heredia, Dirk Sudholt, and Barbora Trubenová (2017):
    Towards a Runtime Comparison of Natural and Artificial Evolution
    Algorithmica 78(2), pages 681-713.
  17. Dogan Corus, Jun He, Thomas Jansen, Pietro S. Oliveto, Dirk Sudholt, and Christine Zarges (2017):
    On Easiest Functions for Mutation Operators in Bio-Inspired Optimisation
    Algorithmica 78(2), pages 714-740.
  18. Dirk Sudholt (2017):
    How Crossover Speeds Up Building-Block Assembly in Genetic Algorithms
    Evolutionary Computation, 25(2), pages 237-274.
  19. Alberto Moraglio and Dirk Sudholt (2017):
    Principled Design and Runtime Analysis of Abstract Convex Evolutionary Search
    Evolutionary Computation, 25(2), pages 205-236.
  20. Tiago Paixão, Golnaz Badkobeh, Nick Barton, Doğan Çörüş, Duc-Cuong Dang, Tobias Friedrich, Per Kristian Lehre, Dirk Sudholt, Andrew M. Sutton, and Barbora Trubenová (2015):
    Toward a unifying framework for evolutionary processes
    Journal of Theoretical Biology, Volume 383, pages 28-43.
  21. Andrea Mambrini and Dirk Sudholt (2015):
    Design and Analysis of Schemes for Adapting Migration Intervals in Parallel Evolutionary Algorithms
    Evolutionary Computation, 23(3), pages 559-582.
  22. Joseph Kempka, Phil McMinn, and Dirk Sudholt (2015):
    Design and analysis of different alternating variable searches for search-based software testing
    Theoretical Computer Science, Volume 605, pages 1-20.
  23. Jörg Lässig and Dirk Sudholt (2014):
    Analysis of Speedups in Parallel Evolutionary Algorithms and (1+λ) EAs for Combinatorial Optimization
    Theoretical Computer Science, Volume 551, pages 66-83.
  24. Jörg Lässig and Dirk Sudholt (2014):
    General Upper Bounds on the Running Time of Parallel Evolutionary Algorithms
    Evolutionary Computation, 22(3), pages 405-437.
  25. Leandro L. Minku, Dirk Sudholt, and Xin Yao (2014):
    Improved Evolutionary Algorithm Design for the Project Scheduling Problem Based on Runtime Analysis
    IEEE Transactions on Software Engineering, 40(1), pages 83-102.
  26. Jonathan E. Rowe and Dirk Sudholt (2014):
    The Choice of the Offspring Population Size in the (1,λ) Evolutionary Algorithm
    Theoretical Computer Science, Volume 545, pages 20-38.
  27. Jörg Lässig and Dirk Sudholt (2013):
    Design and analysis of migration in parallel evolutionary algorithms
    Soft Computing, 17(7), pages 1121-1144.
  28. Dirk Sudholt (2013):
    A New Method for Lower Bounds on the Running Time of Evolutionary Algorithms
    IEEE Transactions on Evolutionary Computation, 17(3), pages 418-435.
    Download preprint
  29. Benjamin Doerr and Thomas Jansen and Dirk Sudholt and Carola Winzen and Christine Zarges (2013):
    Mutation Rate Matters Even When Optimizing Monotone Functions.
    Evolutionary Computation, 21(1), pages 1-21.
  30. Dirk Sudholt and Christian Thyssen (2012):
    A Simple Ant Colony Optimizer for Stochastic Shortest Path Problems.
    Algorithmica, 64(4), pages 643-672.
    Download preprint
  31. Dirk Sudholt and Christian Thyssen (2012):
    Running Time Analysis of Ant Colony Optimization for Shortest Path Problems.
    Journal of Discrete Algorithms, Volume 10, January 2012, pages 165-180.
    Download preprint
  32. Benjamin Doerr, Frank Neumann, Dirk Sudholt, and Carsten Witt (2011):
    Runtime analysis of the 1-ANT ant colony optimizer.
    Theoretical Computer Science, 412(17), 1629-1644.
  33. Dirk Sudholt (2011):
    Hybridizing Evolutionary Algorithms with Variable-Depth Search to Overcome Local Optima.
    Algorithmica, 59(3), 343-368.
  34. Dirk Sudholt and Carsten Witt (2010):
    Runtime Analysis of a Binary Particle Swarm Optimizer.
    Theoretical Computer Science, 411(21), 2084-2100.
  35. Thomas Sauerwald and Dirk Sudholt (2010):
    A Self-stabilizing Algorithm for Cut Problems in Synchronous Networks.
    Theoretical Computer Science, 411(14-15), 1599-1612.
  36. Thomas Jansen and Dirk Sudholt (2010):
    Analysis of an Asymmetric Mutation Operator.
    Evolutionary Computation, 18(1), 1-26.
  37. Tobias Friedrich, Pietro S. Oliveto, Dirk Sudholt, and Carsten Witt (2009):
    Analysis of Diversity-Preserving Mechanisms for Global Exploration.
    Evolutionary Computation, 17(4), 455-476.
  38. Dirk Sudholt (2009):
    The Impact of Parametrization in Memetic Evolutionary Algorithms.
    Theoretical Computer Science, 410(26), 2511-2528.
  39. Frank Neumann, Dirk Sudholt, and Carsten Witt (2009):
    Analysis of different MMAS ACO algorithms on unimodal functions and plateaus.
    Swarm Intelligence, 3(1), 35-68.
  40. Conference Articles

  41. Edgar Covantes Osuna and Dirk Sudholt (2018):
    Empirical Analysis of Diversity-preserving Mechanisms on Example Landscapes for Multimodal Optimisation
    Accepted for Parallel Problem from Nature (PPSN 2018). Springer.
  42. Dirk Sudholt (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). ACM Press, pages 1523-1530.
  43. Johannes Lengler, Dirk Sudholt, and Carsten Witt (2018):
    Medium Step Sizes are Harmful for the Compact Genetic Algorithm
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2018). ACM Press, pages 1499-1506.
  44. Edgar Covantes Osuna and Dirk Sudholt (2018):
    Runtime Analysis of Probabilistic Crowding and Restricted Tournament Selection for Bimodal Optimisation
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2018). ACM Press, pages 929-936.
    Nominated for a best paper award in the track 'Genetic Algorithms'.
    Download preprint
  45. Phan Trung Hai Nguyen and Dirk Sudholt (2018):
    Memetic Algorithms Beat Evolutionary Algorithms on the Class of Hurdle Problems
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2018). ACM Press, pages 1071-1078.
  46. Andrei Lissovoi, Dirk Sudholt, Markus Wagner, and Christine Zarges (2017):
    Theoretical results on bet-and-run as an initialisation strategy
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2017). ACM Press, pages 857-864.
  47. Edgar Covantes Osuna, Wanru Gao, Frank Neumann, and Dirk Sudholt (2017):
    Speeding Up Evolutionary Multi-objective Optimisation Through Diversity-Based Parent Selection
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2017). ACM Press, pages 553-560.
  48. Samadhi Nallaperuma, Pietro S. Oliveto, Jorge Pérez Heredia, and Dirk Sudholt (2017):
    When is it Beneficial to Reject Improvements?
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2017). ACM Press, pages 1391-1398.
  49. Edgar Covantes Osuna and Dirk Sudholt (2017):
    Analysis of the Clearing Diversity-Preserving Mechanism
    Proceedings of Foundations of Genetic Algorithms (FOGA 2017). ACM Press, pages 55-63.
  50. Duc-Cuong Dang, Tobias Friedrich, Timo Kötzing, Martin S. Krejca, Per Kristian Lehre, Pietro S. Oliveto, Dirk Sudholt, and Andrew M. Sutton (2016):
    Emergence of Diversity and its Benefits for Crossover in Genetic Algorithms
    Proceedings of Parallel Problem Solving from Nature (PPSN 2016). Springer, pages 890-900.
    Nominated for a best paper award.
  51. Duc-Cuong Dang, Tobias Friedrich, Timo Kötzing, Martin S. Krejca, Per Kristian Lehre, Pietro S. Oliveto, Dirk Sudholt, and Andrew M. Sutton (2016):
    Escaping Local Optima with Diversity Mechanisms and Crossover
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2016). ACM Press, pages 645-652.
  52. Pietro S. Oliveto, Tiago Paixão, Jorge Pérez Heredia, Dirk Sudholt, and Barbora Trubenová (2016):
    When Non-Elitism Outperforms Elitism for Crossing Fitness Valleys
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2016). ACM Press, pages 1163-1170.
  53. Dirk Sudholt and Carsten Witt (2016):
    Update Strength in EDAs and ACO: How to Avoid Genetic Drift
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2016). ACM Press, pages 61-68.
    Nominated for a best paper award in the track 'Ant Colony Optimization and Swarm Intelligence'.
    Download full paper
  54. Brian W. Goldman and Dirk Sudholt (2016):
    Runtime Analysis for the Parameter-less Population Pyramid
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2016). ACM Press, pages 669-676.
  55. Tiago Paixão, Jorge Pérez Heredia, Dirk Sudholt, and Barbora Trubenová (2015):
    First Steps Towards a Runtime Comparison of Natural and Artificial Evolution
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2015). ACM Press, pages 1455-1462.
    Download full paper
  56. Dogan Corus, Jun He, Thomas Jansen, Pietro S. Oliveto, Dirk Sudholt, and Christine Zarges (2015):
    On Easiest Functions for Somatic Contiguous Hypermutations And Standard Bit Mutations
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2015). ACM Press, pages 1399-1406.
  57. Golnaz Badkobeh, Per Kristian Lehre, and Dirk Sudholt (2015):
    Black-box Complexity of Parallel Search with Distributed Populations
    Proceedings of Foundations of Genetic Algorithms (FOGA 2015). ACM Press, pages 3-15.
  58. Pietro S. Oliveto, Dirk Sudholt, and Christine Zarges (2014):
    On the Runtime Analysis of Fitness Sharing Mechanisms
    Parallel Problem Solving from Nature (PPSN 2014). LNCS 8672, Springer, pages 932-941.
  59. Golnaz Badkobeh, Per Kristian Lehre, and Dirk Sudholt (2014):
    Unbiased Black-Box Complexity of Parallel Search
    Parallel Problem Solving from Nature (PPSN 2014). LNCS 8672, Springer, pages 892-901.
  60. Andrea Mambrini and Dirk Sudholt (2014):
    Design and Analysis of Adaptive Migration Intervals in Parallel Evolutionary Algorithms
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2014). ACM Press, pages 1047-1054.
    Best paper award in the track 'Parallel Evolutionary Systems'.
  61. Pietro S. Oliveto and Dirk Sudholt (2014):
    On the Runtime Analysis of Stochastic Ageing Mechanisms
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2014). ACM Press, pages 113-120.
    Best paper award in the track 'Artificial Immune Systems'.
  62. Samadhi Nallaperuma, Frank Neumann, and Dirk Sudholt (2014):
    A Fixed Budget Analysis of Randomized Search Heuristics for the Traveling Salesperson Problem
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2014). ACM Press, pages 807-814.
    Nominated for a best paper award in the track 'Genetic Algorithms'.
  63. Joseph Kempka, Phil McMinn, and Dirk Sudholt (2013):
    A Theoretical Runtime and Empirical Analysis of Different Alternating Variable Searches for Search-Based Testing
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2013). ACM press, pages 1445-1452.
  64. Benjamin Doerr, Dirk Sudholt, and Carsten Witt (2013):
    When Do Evolutionary Algorithms Optimize Separable Functions in Parallel?
    Proceedings of the twelfth Workshop on Foundations of Genetic Algorithms (FOGA 2013). ACM Press, pages 51-64.
  65. Andrea Mambrini, Dirk Sudholt, and Xin Yao (2012):
    Homogeneous and Heterogeneous Island Models for the Set Cover Problem
    Parallel Problem Solving from Nature (PPSN 2012). LNCS 7491, Springer, pages 11-20.
  66. Alberto Moraglio and Dirk Sudholt (2012):
    Runtime Analysis of Convex Evolutionary Search
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2012). ACM press, pages 649-656.
    Best paper award in the track 'Genetic Algorithms'.
  67. Leandro L. Minku, Dirk Sudholt, and Xin Yao (2012):
    Evolutionary Algorithms for the Project Scheduling Problem: Runtime Analysis and Improved Design
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2012). ACM press, pages 1221-1228.
  68. Dirk Sudholt (2012):
    Crossover Speeds Up Building-Block Assembly
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2012). ACM press, pages 689-696.
    Nominated for a best paper award in the track 'Genetic Algorithms'.
    Download extended and improved version
  69. Jonathan E. Rowe and Dirk Sudholt (2012):
    The Choice of the Offspring Population Size in the (1,λ) EA
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2012). ACM press, pages 1349-1356.
    Nominated for a best paper award in the tracks 'Theory/ESEP'.
  70. Jörg Lässig and Dirk Sudholt (2011):
    Analysis of Speedups in Parallel Evolutionary Algorithms for Combinatorial Optimization
    Proceedings of the 22nd International Symposium on Algorithms and Computation (ISAAC 2011). LNCS 7074, Springer, pages 405-414.
    Download preliminary version
  71. Timo Kötzing, Dirk Sudholt, and Madeleine Theile (2011):
    How Crossover Helps in Pseudo-Boolean Optimization
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2011). ACM Press, pages 989-996.
    Best paper award in the track 'Genetic Algorithms'.
  72. Frank Neumann, Pietro S. Oliveto, Günter Rudolph, and Dirk Sudholt (2011):
    On the Effectiveness of Crossover for Migration in Parallel Evolutionary Algorithms
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2011). ACM press, pages 1587-1594.
  73. Timo Kötzing, Frank Neumann, Dirk Sudholt, and Markus Wagner (2011):
    Simple Max-Min Ant Systems and the Optimization of Linear Pseudo-Boolean Functions
    Proceedings of the 11th Workshop on Foundations of Genetic Algorithms (FOGA 2011). ACM Press, pages 209-218.
    Download preliminary version
  74. Jörg Lässig and Dirk Sudholt (2011):
    Adaptive Population Models for Offspring Populations and Parallel Evolutionary Algorithms
    Proceedings of the 11th Workshop on Foundations of Genetic Algorithms (FOGA 2011). ACM Press, pages 181-192.
    Download preliminary version
  75. Dirk Sudholt (2011):
    Using Markov-Chain Mixing Time Estimates for the Analysis of Ant Colony Optimization
    Proceedings of the 11th Workshop on Foundations of Genetic Algorithms (FOGA 2011). ACM Press, pages 139-150.
  76. Dirk Sudholt and Christine Zarges (2010):
    Analysis of an Iterated Local Search Algorithm for Vertex Coloring
    Proceedings of the 21st International Symposium on Algorithms and Computation (ISAAC 2010). LNCS 6506, Springer, pages 340-352.
  77. Jörg Lässig and Dirk Sudholt (2010):
    Experimental Supplements to the Theoretical Analysis of Migration in the Island Model
    Parallel Problem Solving from Nature (PPSN 2010). LNCS 6238, Springer, pages 224-233.
  78. Benjamin Doerr and Thomas Jansen and Dirk Sudholt and Carola Winzen and Christine Zarges (2010):
    Optimizing Monotone Functions Can Be Difficult
    Parallel Problem Solving from Nature (PPSN 2010). LNCS 6238, Springer, pages 42-51.
  79. Jörg Lässig and Dirk Sudholt (2010):
    General Scheme for Analyzing Running Times of Parallel Evolutionary Algorithms
    Parallel Problem Solving from Nature (PPSN 2010). LNCS 6238, Springer, pages 234-243.
    Best paper award.
    download extended journal version
  80. Dirk Sudholt (2010):
    General Lower Bounds for the Running Time of Evolutionary Algorithms
    Parallel Problem Solving from Nature (PPSN 2010). LNCS 6238, Springer, pages 124-133.
    Download full conference version, download extended journal version
  81. Jörg Lässig and Dirk Sudholt (2010):
    The Benefit of Migration in Parallel Evolutionary Algorithms.
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2010). ACM Press, pages 1105-1112.
    Best paper award in the track 'Parallel Evolutionary Systems'.
  82. Christian Horoba and Dirk Sudholt (2010):
    Ant Colony Optimization for Stochastic Shortest Path Problems.
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2010). ACM Press, pages 1465-1472.
    Nominated for a best paper award in the track 'Theory'.
  83. Frank Neumann, Dirk Sudholt and Carsten Witt (2010):
    A Few Ants are Enough: ACO with Iteration-Best Update.
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2010). ACM Press, pages 63-70.
    Nominated for a best paper award in the track 'ACO/Swarm Intelligence'.
  84. Christian Horoba and Dirk Sudholt (2009):
    Running Time Analysis of ACO Systems for Shortest Path Problems
    Engineering Stochastic Local Search Algorithms (SLS 2009). LNCS 5752, Springer, pages 76-91.
    Download preprint
  85. Frank Neumann, Dirk Sudholt, and Carsten Witt (2008):
    Rigorous Analyses for the Combination of Ant Colony Optimization and Local Search.
    Proceedings of the Sixth International Conference on Ant Colony Optimization and Swarm Intelligence (ANTS 2008). LNCS 5217, Springer, pages 132-143.
    Download technical report
  86. Thomas Sauerwald and Dirk Sudholt (2008):
    Self-stabilizing Cuts in Synchronous Networks.
    Proceedings of the 15th International Colloquium on Structural Information and Communication Complexity (SIROCCO 2008). LNCS 5058, Springer, pages 234-246.
    Download technical report
  87. Dirk Sudholt and Carsten Witt (2008):
    Runtime Analysis of Binary PSO.
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2008). ACM Press, pages 135-142.
    Download technical report
  88. Dirk Sudholt (2008):
    Memetic Algorithms with Variable-Depth Search to Overcome Local Optima.
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2008). ACM Press, pages 787-794.
    Nominated for a best paper award in the track 'Formal Theory'.
    Download technical report
  89. Tobias Friedrich, Pietro Oliveto, Dirk Sudholt, and Carsten Witt (2008):
    Theoretical Analysis of Diversity Mechanisms for Global Exploration.
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2008). ACM Press, pages 945-952.
    Best paper award in the track 'Genetic Algorithms'.
    Download technical report
  90. Frank Neumann, Dirk Sudholt, and Carsten Witt (2007):
    Comparing Variants of MMAS ACO Algorithms on Pseudo-Boolean Functions.
    Engineering Stochastic Local Search Algorithms (SLS 2007). LNCS 4638, Springer, pages 61-75.
    Download technical report
  91. Benjamin Doerr, Frank Neumann, Dirk Sudholt, and Carsten Witt (2007):
    On the Runtime Analysis of the 1-ANT ACO Algorithm.
    Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2007). ACM Press, pages 33-40.
    Best paper award in the track 'Ant Colony Optimization, Swarm Intelligence, and Artificial Immune Systems'.
    Download technical report
  92. Dirk Sudholt (2006):
    Local Search in Evolutionary Algorithms: the Impact of the Local Search Frequency.
    In Tetsuo Asano (Ed.): Proceedings of the 17th International Symposium on Algorithms and Computation (ISAAC 2006). LNCS 4288, Springer, pages 359-368.
    Download more detailed technical report
  93. Dirk Sudholt (2006):
    On the Analysis of the (1+1) Memetic Algorithm.
    In Maarten Keijzer et al. (Eds.): Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2006). ACM Press, pages 493-500.
    Download technical report
  94. Thomas Jansen and Dirk Sudholt (2005):
    Design and Analysis of an Asymmetric Mutation Operator.
    Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2005). IEEE Press, pages 497-504.
    Download revised technical report
  95. Dirk Sudholt (2005):
    Crossover is Provably Essential for the Ising Model on Trees.
    In H.-G. Beyer et al. (Eds.): Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2005). ACM Press, pages 1161-1167.
    Download technical report
  96. Patrick Briest, Dimo Brockhoff, Bastian Degener, Matthias Englert, Christian Gunia, Oliver Heering, Thomas Jansen, Michael Leifhelm, Kai Plociennik, Heiko Röglin, Andrea Schweer, Dirk Sudholt, Stefan Tannenbaum, and Ingo Wegener (2004):
    Experimental Supplements to the Theoretical Analysis of EAs on Problems from Combinatorial Optimization.
    In Xin Yao et al. (Eds.): Proceedings of the 8th International Conference on Parallel Problem Solving From Nature (PPSN VIII). LNCS 3242, Springer, pages 21-30.
    Download technical report
  97. Patrick Briest, Dimo Brockhoff, Bastian Degener, Matthias Englert, Christian Gunia, Oliver Heering, Thomas Jansen, Michael Leifhelm, Kai Plociennik, Heiko Röglin, Andrea Schweer, Dirk Sudholt, Stefan Tannenbaum, and Ingo Wegener (2004):
    The Ising Model: Simple Evolutionary Algorithms as Adaptation Schemes.
    In Xin Yao et al. (Eds.): Proceedings of the 8th International Conference on Parallel Problem Solving From Nature (PPSN VIII). LNCS 3242, Springer, pages 31-40.
    Download technical report
  98. Theses

  99. Dirk Sudholt (2008):
    Computational Complexity of Evolutionary Algorithms, Hybridizations, and Swarm Intelligence
    Dissertation, Technische Universität Dortmund

    Download PDF version

  100. Dirk Sudholt (2004):
    Evolutionäre Algorithmen als Adaptationsschemata (Evolutionary Algorithms as Adaptation Schemes)
    Diploma Thesis, Universität Dortmund

    Available online (in German):  Postscript (1.2 MB) /  zipped Postscript (398.5 KB) /  PDF (1.1 MB)