default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDDirk Sudholt
Person information
- affiliation: University of Passau, Germany
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[j50]Mario Alejandro Hevia Fajardo
, Dirk Sudholt:
Self-adjusting offspring population sizes outperform fixed parameters on the cliff function. Artif. Intell. 328: 104061 (2024)- [j49]Duc-Cuong Dang, Andre Opris, Dirk Sudholt:
Crossover can guarantee exponential speed-ups in evolutionary multi-objective optimisation. Artif. Intell. 330: 104098 (2024) - [j48]Mario Alejandro Hevia Fajardo, Dirk Sudholt:
Self-adjusting Population Sizes for Non-elitist Evolutionary Algorithms: Why Success Rates Matter. Algorithmica 86(2): 526-565 (2024) - [j47]Johannes Lengler, Andre Opris, Dirk Sudholt:
Analysing Equilibrium States for Population Diversity. Algorithmica 86(7): 1-35 (2024) - [j46]Jakob Bossek, Dirk Sudholt:
Runtime Analysis of Quality Diversity Algorithms. Algorithmica 86(10): 3252-3283 (2024) - [c101]Jakob Baumann, Ignaz Rutter, Dirk Sudholt:
Evolutionary Algorithms for One-Sided Bipartite Crossing Minimisation (Poster Abstract). GD 2024: 51:1-51:3 - [c100]Jakob Baumann, Ignaz Rutter, Dirk Sudholt:
Evolutionary Computation Meets Graph Drawing: Runtime Analysis for Crossing Minimisation on Layered Graph Drawings. GECCO 2024 - [c99]Duc-Cuong Dang, Andre Opris, Dirk Sudholt:
Illustrating the Efficiency of Popular Evolutionary Multi-Objective Algorithms Using Runtime Analysis. GECCO 2024 - [c98]Andre Opris, Duc-Cuong Dang, Frank Neumann, Dirk Sudholt:
Runtime Analyses of NSGA-III on Many-Objective Problems. GECCO 2024 - [c97]Andre Opris, Duc-Cuong Dang, Dirk Sudholt:
Hot of the Press: Crossover Can Guarantee Exponential Speed-Ups in Evolutionary Multi-Objective Optimisation. GECCO Companion 2024: 51-52 - [c96]Andre Opris, Johannes Lengler, Dirk Sudholt:
A Tight O(4k/pc) Runtime Bound for a (μ+1)GA on Jumpk for Realistic Crossover Probabilities. GECCO 2024 - [c95]Marcus Schmidbauer, Andre Opris, Jakob Bossek, Frank Neumann, Dirk Sudholt:
Guiding Quality Diversity on Monotone Submodular Functions: Customising the Feature Space by Adding Boolean Conjunctions. GECCO 2024 - [c94]Dirk Sudholt, Giovanni Squillero:
Theory and Practice of Population Diversity in Evolutionary Computation. GECCO Companion 2024: 1391-1409 - [c93]Duc-Cuong Dang, Andre Opris, Dirk Sudholt:
On the Equivalence Between Stochastic Tournament and Power-Law Ranking Selection and How to Implement Them Efficiently. PPSN (3) 2024: 230-245 - [c92]Duc-Cuong Dang, Andre Opris, Dirk Sudholt:
Level-Based Theorems for Runtime Analysis of Multi-objective Evolutionary Algorithms. PPSN (3) 2024: 246-263 - [i37]Andre Opris, Johannes Lengler, Dirk Sudholt:
A Tight O(4k/pc) Runtime Bound for a (μ+1) GA on Jumpk for Realistic Crossover Probabilities. CoRR abs/2404.07061 (2024) - [i36]Andre Opris, Duc-Cuong Dang, Frank Neumann, Dirk Sudholt:
Runtime Analyses of NSGA-III on Many-Objective Problems. CoRR abs/2404.11433 (2024) - [i35]Duc-Cuong Dang, Andre Opris, Dirk Sudholt:
Illustrating the Efficiency of Popular Evolutionary Multi-Objective Algorithms Using Runtime Analysis. CoRR abs/2405.13572 (2024) - [i34]Jakob Baumann, Ignaz Rutter, Dirk Sudholt:
Evolutionary Algorithms for One-Sided Bipartite Crossing Minimisation. CoRR abs/2409.15312 (2024) - 2023
- [j45]Jakob Bossek, Dirk Sudholt:
Do additional target points speed up evolutionary algorithms? Theor. Comput. Sci. 950: 113757 (2023) - [c91]Duc-Cuong Dang, Andre Opris, Bahare Salehi, Dirk Sudholt:
A Proof That Using Crossover Can Guarantee Exponential Speed-Ups in Evolutionary Multi-Objective Optimisation. AAAI 2023: 12390-12398 - [c90]Carlo Kneissl, Dirk Sudholt:
The Cost of Randomness in Evolutionary Algorithms: Crossover can Save Random Bits. EvoCOP 2023: 179-194 - [c89]Duc-Cuong Dang, Andre Opris, Bahare Salehi, Dirk Sudholt:
Analysing the Robustness of NSGA-II under Noise. GECCO 2023: 642-651 - [c88]Dirk Sudholt, Giovanni Squillero:
Theory and Practice of Population Diversity in Evolutionary Computation. GECCO Companion 2023: 1361-1378 - [c87]Jakob Bossek, Dirk Sudholt:
Runtime Analysis of Quality Diversity Algorithms. GECCO 2023: 1546-1554 - [c86]Joost Jorritsma, Johannes Lengler, Dirk Sudholt:
Comma Selection Outperforms Plus Selection on OneMax with Randomly Planted Optima. GECCO 2023: 1602-1610 - [c85]Johannes Lengler, Andre Opris, Dirk Sudholt:
Analysing Equilibrium States for Population Diversity. GECCO 2023: 1628-1636 - [i33]Duc-Cuong Dang, Andre Opris, Bahare Salehi, Dirk Sudholt:
A Proof that Using Crossover Can Guarantee Exponential Speed-Ups in Evolutionary Multi-Objective Optimisation. CoRR abs/2301.13687 (2023) - [i32]Johannes Lengler, Andre Opris, Dirk Sudholt:
Analysing Equilibrium States for Population Diversity. CoRR abs/2304.09690 (2023) - [i31]Joost Jorritsma, Johannes Lengler, Dirk Sudholt:
Comma Selection Outperforms Plus Selection on OneMax with Randomly Planted Optima. CoRR abs/2304.09712 (2023) - [i30]Jakob Bossek, Dirk Sudholt:
Runtime Analysis of Quality Diversity Algorithms. CoRR abs/2305.18966 (2023) - [i29]Duc-Cuong Dang, Andre Opris, Bahare Salehi, Dirk Sudholt:
Analysing the Robustness of NSGA-II under Noise. CoRR abs/2306.04525 (2023) - 2022
- [j44]George T. Hall, Pietro S. Oliveto, Dirk Sudholt:
On the impact of the performance metric on efficient algorithm configuration. Artif. Intell. 303: 103629 (2022) - [j43]Pietro S. Oliveto, Dirk Sudholt, Carsten Witt:
Tight Bounds on the Expected Runtime of a Standard Steady State Genetic Algorithm. Algorithmica 84(6): 1603-1658 (2022) - [j42]Edgar Covantes Osuna, Dirk Sudholt:
Runtime Analysis of Restricted Tournament Selection for Bimodal Optimisation. Evol. Comput. 30(1): 1-26 (2022) - [j41]Mario Alejandro Hevia Fajardo, Dirk Sudholt:
Theoretical and Empirical Analysis of Parameter Control Mechanisms in the (1 + (λ, λ)) Genetic Algorithm. ACM Trans. Evol. Learn. Optim. 2(4): 13:1-13:39 (2022) - [c84]Mario Alejandro Hevia Fajardo, Dirk Sudholt:
Hard problems are easier for success-based parameter control. GECCO 2022: 796-804 - [c83]Frank Neumann, Dirk Sudholt, Carsten Witt:
The compact genetic algorithm struggles on Cliff functions. GECCO 2022: 1426-1433 - [c82]Dirk Sudholt, Giovanni Squillero:
Theory and practice of population diversity in evolutionary computation. GECCO Companion 2022: 1469-1486 - [c81]Vijay Dhanjibhai Bhuva, Duc-Cuong Dang, Liam Huber, Dirk Sudholt:
Evolutionary Algorithms for Cardinality-Constrained Ising Models. PPSN (2) 2022: 456-469 - [i28]Edgar Covantes Osuna, Dirk Sudholt:
Runtime Analysis of Restricted Tournament Selection for Bimodal Optimisation. CoRR abs/2201.06485 (2022) - [i27]Frank Neumann, Dirk Sudholt, Carsten Witt:
The Compact Genetic Algorithm Struggles on Cliff Functions. CoRR abs/2204.04904 (2022) - [i26]Mario Alejandro Hevia Fajardo, Dirk Sudholt:
Hard Problems are Easier for Success-based Parameter Control. CoRR abs/2204.05817 (2022) - 2021
- [j40]Dirk Sudholt:
Analysing the Robustness of Evolutionary Algorithms to Noise: Refined Runtime Bounds and an Example Where Noise is Beneficial. Algorithmica 83(4): 976-1011 (2021) - [j39]Johannes Lengler, Dirk Sudholt, Carsten Witt:
The Complex Parameter Landscape of the Compact Genetic Algorithm. Algorithmica 83(4): 1096-1137 (2021) - [j38]Jakob Bossek, Frank Neumann, Pan Peng, Dirk Sudholt:
Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem. Algorithmica 83(10): 3148-3179 (2021) - [c80]Mario Alejandro Hevia Fajardo, Dirk Sudholt:
Self-adjusting offspring population sizes outperform fixed parameters on the cliff function. FOGA 2021: 5:1-5:15 - [c79]Jakob Bossek, Dirk Sudholt:
Do additional optima speed up evolutionary algorithms? FOGA 2021: 8:1-8:11 - [c78]Mario Alejandro Hevia Fajardo, Dirk Sudholt:
Self-adjusting population sizes for non-elitist evolutionary algorithms: why success rates matter. GECCO 2021: 1151-1159 - [i25]Mario Alejandro Hevia Fajardo, Dirk Sudholt:
Self-Adjusting Population Sizes for Non-Elitist Evolutionary Algorithms: Why Success Rates Matter. CoRR abs/2104.05624 (2021) - [i24]Jakob Bossek, Frank Neumann, Pan Peng, Dirk Sudholt:
Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem. CoRR abs/2105.12525 (2021) - 2020
- [j37]Phan Trung Hai Nguyen, Dirk Sudholt:
Memetic algorithms outperform evolutionary algorithms in multimodal optimisation. Artif. Intell. 287: 103345 (2020) - [j36]Edgar Covantes Osuna, Wanru Gao, Frank Neumann, Dirk Sudholt:
Design and analysis of diversity-based parent selection schemes for speeding up evolutionary multi-objective optimisation. Theor. Comput. Sci. 832: 123-142 (2020) - [j35]Edgar Covantes Osuna, Dirk Sudholt:
Runtime Analysis of Crowding Mechanisms for Multimodal Optimization. IEEE Trans. Evol. Comput. 24(3): 581-592 (2020) - [j34]Per Kristian Lehre, Dirk Sudholt:
Parallel Black-Box Complexity With Tail Bounds. IEEE Trans. Evol. Comput. 24(6): 1010-1024 (2020) - [c77]Michael Foster, Matthew Hughes, George O. O'Brien, Pietro S. Oliveto, James Pyle, Dirk Sudholt, James Williams:
Do sophisticated evolutionary algorithms perform better than simple ones? GECCO 2020: 184-192 - [c76]George T. Hall, Pietro S. Oliveto, Dirk Sudholt:
Analysis of the performance of algorithm configurators for search heuristics with global mutation operators. GECCO 2020: 823-831 - [c75]Mario Alejandro Hevia Fajardo, Dirk Sudholt:
On the choice of the parameter control mechanism in the (1+(λ, λ)) genetic algorithm. GECCO 2020: 832-840 - [c74]Dirk Sudholt, Giovanni Squillero:
Theory and practice of population diversity in evolutionary computation. GECCO Companion 2020: 975-992 - [c73]Nasser M. Albunian, Gordon Fraser, Dirk Sudholt:
Causes and effects of fitness landscapes in unit test generation. GECCO 2020: 1204-1212 - [c72]Jakob Bossek, Frank Neumann, Pan Peng, Dirk Sudholt:
More effective randomized search heuristics for graph coloring through dynamic optimization. GECCO 2020: 1277-1285 - [c71]Pietro S. Oliveto, Dirk Sudholt, Carsten Witt:
A tight lower bound on the expected runtime of standard steady state genetic algorithms. GECCO 2020: 1323-1331 - [c70]George T. Hall, Pietro S. Oliveto, Dirk Sudholt:
Fast Perturbative Algorithm Configurators. PPSN (1) 2020: 19-32 - [c69]Nasser M. Albunian, Gordon Fraser, Dirk Sudholt:
Measuring and Maintaining Population Diversity in Search-Based Unit Test Generation. SSBSE 2020: 153-168 - [p5]Dirk Sudholt:
The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses. Theory of Evolutionary Computation 2020: 359-404 - [i23]George T. Hall, Pietro S. Oliveto, Dirk Sudholt:
Analysis of the Performance of Algorithm Configurators for Search Heuristics with Global Mutation Operators. CoRR abs/2004.04519 (2020) - [i22]Jakob Bossek, Frank Neumann, Pan Peng, Dirk Sudholt:
More Effective Randomized Search Heuristics for Graph Coloring Through Dynamic Optimization. CoRR abs/2005.13825 (2020) - [i21]George T. Hall, Pietro Simone Oliveto, Dirk Sudholt:
Fast Perturbative Algorithm Configurators. CoRR abs/2007.03336 (2020)
2010 – 2019
- 2019
- [j33]Carola Doerr, Dirk Sudholt:
Preface to the Special Issue on Theory of Genetic and Evolutionary Computation. Algorithmica 81(2): 589-592 (2019) - [j32]Samadhi Nallaperuma, Pietro S. Oliveto, Jorge Pérez Heredia, Dirk Sudholt:
On the Analysis of Trajectory-Based Search Algorithms: When is it Beneficial to Reject Improvements? Algorithmica 81(2): 858-885 (2019) - [j31]Dirk Sudholt, Carsten Witt:
On the Choice of the Update Strength in Estimation-of-Distribution Algorithms and Ant Colony Optimization. Algorithmica 81(4): 1450-1489 (2019) - [j30]Edgar Covantes Osuna, Dirk Sudholt:
On the Runtime Analysis of the Clearing Diversity-Preserving Mechanism. Evol. Comput. 27(3): 403-433 (2019) - [j29]Pietro S. Oliveto, Dirk Sudholt, Christine Zarges:
On the benefits and risks of using fitness sharing for multimodal optimisation. Theor. Comput. Sci. 773: 53-70 (2019) - [c68]Jakob Bossek, Dirk Sudholt:
Time complexity analysis of RLS and (1 + 1) EA for the edge coloring problem. FOGA 2019: 102-115 - [c67]George T. Hall, Pietro S. Oliveto, Dirk Sudholt:
On the impact of the cutoff time on the performance of algorithm configurators. GECCO 2019: 907-915 - [c66]Jakob Bossek, Frank Neumann, Pan Peng, Dirk Sudholt:
Runtime analysis of randomized search heuristics for dynamic graph coloring. GECCO 2019: 1443-1451 - [i20]Per Kristian Lehre, Dirk Sudholt:
Parallel Black-Box Complexity with Tail Bounds. CoRR abs/1902.00107 (2019) - [i19]George T. Hall, Pietro S. Oliveto, Dirk Sudholt:
On the Impact of the Cutoff Time on the Performance of Algorithm Configurators. CoRR abs/1904.06230 (2019) - 2018
- [j28]Timo Kötzing, Dirk Sudholt:
Preface to the Special Issue on Theory of Genetic and Evolutionary Computation. Algorithmica 80(5): 1575-1578 (2018) - [j27]Pietro S. Oliveto, Tiago Paixão, Jorge Pérez Heredia, Dirk Sudholt, Barbora Trubenová:
How to Escape Local Optima in Black Box Optimisation: When Non-elitism Outperforms Elitism. Algorithmica 80(5): 1604-1633 (2018) - [j26]Duc-Cuong Dang, Tobias Friedrich, Timo Kötzing, Martin S. Krejca, Per Kristian Lehre, Pietro S. Oliveto, Dirk Sudholt, Andrew M. Sutton:
Escaping Local Optima Using Crossover With Emergent Diversity. IEEE Trans. Evol. Comput. 22(3): 484-497 (2018) - [c65]Edgar Covantes Osuna, Dirk Sudholt:
Runtime analysis of probabilistic crowding and restricted tournament selection for bimodal optimisation. GECCO 2018: 929-936 - [c64]Phan Trung Hai Nguyen, Dirk Sudholt:
Memetic algorithms beat evolutionary algorithms on the class of hurdle problems. GECCO 2018: 1071-1078 - [c63]Johannes Lengler, Dirk Sudholt, Carsten Witt:
Medium step sizes are harmful for the compact genetic algorithm. GECCO 2018: 1499-1506 - [c62]Dirk Sudholt:
On the robustness of evolutionary algorithms to noise: refined results and an example where noise helps. GECCO 2018: 1523-1530 - [c61]Edgar Covantes Osuna, Dirk Sudholt:
Empirical Analysis of Diversity-Preserving Mechanisms on Example Landscapes for Multimodal Optimisation. PPSN (2) 2018: 207-219 - [c60]Gisele Lobo Pappa, Michael T. M. Emmerich, Ana L. C. Bazzan, Will N. Browne, Kalyanmoy Deb, Carola Doerr, Marko Durasevic, Michael G. Epitropakis, Saemundur O. Haraldsson, Domagoj Jakobovic, Pascal Kerschke, Krzysztof Krawiec, Per Kristian Lehre, Xiaodong Li, Andrei Lissovoi, Pekka Malo, Luis Martí, Yi Mei, Juan Julián Merelo Guervós, Julian F. Miller, Alberto Moraglio, Antonio J. Nebro, Su Nguyen, Gabriela Ochoa, Pietro S. Oliveto, Stjepan Picek, Nelishia Pillay, Mike Preuss, Marc Schoenauer, Roman Senkerik, Ankur Sinha, Ofer M. Shir, Dirk Sudholt, L. Darrell Whitley, Mark Wineberg, John R. Woodward, Mengjie Zhang:
Tutorials at PPSN 2018. PPSN (2) 2018: 477-489 - [i18]Dirk Sudholt:
The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses. CoRR abs/1801.10087 (2018) - [i17]Edgar Covantes Osuna, Dirk Sudholt:
On the Runtime Analysis of the Clearing Diversity-Preserving Mechanism. CoRR abs/1803.09715 (2018) - [i16]Edgar Covantes Osuna, Dirk Sudholt:
Runtime Analysis of Probabilistic Crowding and Restricted Tournament Selection for Bimodal Optimisation. CoRR abs/1803.09766 (2018) - [i15]Phan Trung Hai Nguyen, Dirk Sudholt:
Memetic Algorithms Beat Evolutionary Algorithms on the Class of Hurdle Problems. CoRR abs/1804.06173 (2018) - [i14]Edgar Covantes Osuna, Wanru Gao, Frank Neumann, Dirk Sudholt:
Design and Analysis of Diversity-Based Parent Selection Schemes for Speeding Up Evolutionary Multi-objective Optimisation. CoRR abs/1805.01221 (2018) - [i13]Dirk Sudholt:
Analysing the Robustness of Evolutionary Algorithms to Noise: Refined Runtime Bounds and an Example Where Noise is Beneficial. CoRR abs/1812.00966 (2018) - 2017
- [j25]Tiago Paixão, Jorge Pérez Heredia, Dirk Sudholt, Barbora Trubenová:
Towards a Runtime Comparison of Natural and Artificial Evolution. Algorithmica 78(2): 681-713 (2017) - [j24]Dogan Corus, Jun He, Thomas Jansen, Pietro S. Oliveto, Dirk Sudholt, Christine Zarges:
On Easiest Functions for Mutation Operators in Bio-Inspired Optimisation. Algorithmica 78(2): 714-740 (2017) - [j23]Alberto Moraglio, Dirk Sudholt:
Principled Design and Runtime Analysis of Abstract Convex Evolutionary Search. Evol. Comput. 25(2): 205-236 (2017) - [j22]Dirk Sudholt:
How Crossover Speeds up Building Block Assembly in Genetic Algorithms. Evol. Comput. 25(2): 237-274 (2017) - [j21]Samadhi Nallaperuma, Frank Neumann, Dirk Sudholt:
Expected Fitness Gains of Randomized Search Heuristics for the Traveling Salesperson Problem. Evol. Comput. 25(4) (2017) - [c59]Edgar Covantes Osuna, Dirk Sudholt:
Analysis of the Clearing Diversity-Preserving Mechanism. FOGA 2017: 55-63 - [c58]Edgar Covantes Osuna, Wanru Gao, Frank Neumann, Dirk Sudholt:
Speeding up evolutionary multi-objective optimisation through diversity-based parent selection. GECCO 2017: 553-560 - [c57]Andrei Lissovoi, Dirk Sudholt, Markus Wagner, Christine Zarges:
Theoretical results on bet-and-run as an initialisation strategy. GECCO 2017: 857-864 - [c56]Dirk Sudholt:
Theory of swarm intelligence: tutorial at GECCO 2017. GECCO (Companion) 2017: 902-921 - [c55]Samadhi Nallaperuma, Pietro S. Oliveto, Jorge Pérez Heredia, Dirk Sudholt:
When is it beneficial to reject improvements? GECCO 2017: 1391-1398 - [e1]Christian Igel, Dirk Sudholt, Carsten Witt:
Proceedings of the 14th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, FOGA 2017, Copenhagen, Denmark, January 12-15, 2017. ACM 2017, ISBN 978-1-4503-4651-1 [contents] - 2016
- [c54]Dirk Sudholt, Carsten Witt:
Update Strength in EDAs and ACO: How to Avoid Genetic Drift. GECCO 2016: 61-68 - [c53]Duc-Cuong Dang, Tobias Friedrich, Timo Kötzing, Martin S. Krejca, Per Kristian Lehre, Pietro S. Oliveto, Dirk Sudholt, Andrew M. Sutton:
Escaping Local Optima with Diversity Mechanisms and Crossover. GECCO 2016: 645-652 - [c52]Brian W. Goldman, Dirk Sudholt:
Runtime Analysis for the Parameter-less Population Pyramid. GECCO 2016: 669-676 - [c51]Dirk Sudholt:
Theory of Swarm Intelligence. GECCO (Companion) 2016: 715-734 - [c50]Pietro S. Oliveto, Tiago Paixão, Jorge Pérez Heredia, Dirk Sudholt, Barbora Trubenová:
When Non-Elitism Outperforms Elitism for Crossing Fitness Valleys. GECCO 2016: 1163-1170 - [c49]Duc-Cuong Dang, Tobias Friedrich, Timo Kötzing, Martin S. Krejca, Per Kristian Lehre, Pietro S. Oliveto, Dirk Sudholt, Andrew M. Sutton:
Emergence of Diversity and Its Benefits for Crossover in Genetic Algorithms. PPSN 2016: 890-900 - [c48]Carola Doerr, Nicolas Bredèche, Enrique Alba, Thomas Bartz-Beielstein, Dimo Brockhoff, Benjamin Doerr, Gusz Eiben, Michael G. Epitropakis, Carlos M. Fonseca, Andreia P. Guerreiro, Evert Haasdijk, Jacqueline Heinerman, Julien Hubert, Per Kristian Lehre, Luigi Malagò, Juan Julián Merelo Guervós, Julian Francis Miller, Boris Naujoks, Pietro S. Oliveto, Stjepan Picek, Nelishia Pillay, Mike Preuss, Patricia Ryser-Welch, Giovanni Squillero, Jörg Stork, Dirk Sudholt, Alberto Paolo Tonda, L. Darrell Whitley, Martin Zaefferer:
Tutorials at PPSN 2016. PPSN 2016: 1012-1022 - [i12]Dirk Sudholt, Carsten Witt:
Update Strength in EDAs and ACO: How to Avoid Genetic Drift. CoRR abs/1607.04063 (2016) - [i11]Duc-Cuong Dang, Tobias Friedrich, Timo Kötzing, Martin S. Krejca, Per Kristian Lehre, Pietro S. Oliveto, Dirk Sudholt, Andrew M. Sutton:
Escaping Local Optima using Crossover with Emergent or Reinforced Diversity. CoRR abs/1608.03123 (2016) - 2015
- [j20]Andrea Mambrini, Dirk Sudholt:
Design and Analysis of Schemes for Adapting Migration Intervals in Parallel Evolutionary Algorithms. Evol. Comput. 23(4): 559-582 (2015) - [j19]Joseph Kempka, Phil McMinn, Dirk Sudholt:
Design and analysis of different alternating variable searches for search-based software testing. Theor. Comput. Sci. 605: 1-20 (2015) - [c47]Golnaz Badkobeh, Per Kristian Lehre, Dirk Sudholt:
Black-box Complexity of Parallel Search with Distributed Populations. FOGA 2015: 3-15 - [c46]Dirk Sudholt:
Theory of Swarm Intelligence. GECCO (Companion) 2015: 451-471 - [c45]Dogan Corus, Jun He, Thomas Jansen, Pietro S. Oliveto, Dirk Sudholt, Christine Zarges:
On Easiest Functions for Somatic Contiguous Hypermutations And Standard Bit Mutations. GECCO 2015: 1399-1406 - [c44]Tiago Paixão, Jorge Pérez Heredia, Dirk Sudholt, Barbora Trubenová:
First Steps Towards a Runtime Comparison of Natural and Artificial Evolution. GECCO 2015: 1455-1462 - [p4]Dirk Sudholt:
Parallel Evolutionary Algorithms. Handbook of Computational Intelligence 2015: 929-959 - [i10]Tiago Paixão, Jorge Pérez Heredia, Dirk Sudholt, Barbora Trubenová:
First Steps Towards a Runtime Comparison of Natural and Artificial Evolution. CoRR abs/1504.06260 (2015) - 2014
- [j18]Jörg Lässig, Dirk Sudholt:
General Upper Bounds on the Runtime of Parallel Evolutionary Algorithms. Evol. Comput. 22(3): 405-437 (2014) - [j17]Jonathan E. Rowe, Dirk Sudholt:
The choice of the offspring population size in the (1, λ) evolutionary algorithm. Theor. Comput. Sci. 545: 20-38 (2014) - [j16]Jörg Lässig, Dirk Sudholt:
Analysis of speedups in parallel evolutionary algorithms and (1+λ) EAs for combinatorial optimization. Theor. Comput. Sci. 551: 66-83 (2014) - [j15]Leandro L. Minku, Dirk Sudholt, Xin Yao:
Improved Evolutionary Algorithm Design for the Project Scheduling Problem Based on Runtime Analysis. IEEE Trans. Software Eng. 40(1): 83-102 (2014) - [c43]Pietro S. Oliveto, Dirk Sudholt:
On the runtime analysis of stochastic ageing mechanisms. GECCO 2014: 113-120 - [c42]Dirk Sudholt:
Theory of swarm intelligence. GECCO (Companion) 2014: 687-708 - [c41]Samadhi Nallaperuma, Frank Neumann, Dirk Sudholt:
A fixed budget analysis of randomized search heuristics for the traveling salesperson problem. GECCO 2014: 807-814 - [c40]Andrea Mambrini, Dirk Sudholt:
Design and analysis of adaptive migration intervals in parallel evolutionary algorithms. GECCO 2014: 1047-1054 - [c39]Golnaz Badkobeh, Per Kristian Lehre, Dirk Sudholt:
Unbiased Black-Box Complexity of Parallel Search. PPSN 2014: 892-901 - [c38]Pietro S. Oliveto, Dirk Sudholt, Christine Zarges:
On the Runtime Analysis of Fitness Sharing Mechanisms. PPSN 2014: 932-941 - [i9]Dirk Sudholt:
How Crossover Speeds Up Building-Block Assembly in Genetic Algorithms. CoRR abs/1403.6600 (2014) - 2013
- [j14]Benjamin Doerr, Thomas Jansen, Dirk Sudholt, Carola Winzen, Christine Zarges:
Mutation Rate Matters Even When Optimizing Monotonic Functions. Evol. Comput. 21(1): 1-27 (2013) - [j13]Jörg Lässig, Dirk Sudholt:
Design and analysis of migration in parallel evolutionary algorithms. Soft Comput. 17(7): 1121-1144 (2013) - [j12]Dirk Sudholt:
A New Method for Lower Bounds on the Running Time of Evolutionary Algorithms. IEEE Trans. Evol. Comput. 17(3): 418-435 (2013) - [c37]Benjamin Doerr, Dirk Sudholt, Carsten Witt:
When do evolutionary algorithms optimize separable functions in parallel? FOGA 2013: 51-64 - [c36]Joseph Kempka, Phil McMinn, Dirk Sudholt:
A theoretical runtime and empirical analysis of different alternating variable searches for search-based testing. GECCO 2013: 1445-1452 - 2012
- [j11]Dirk Sudholt, Christian Thyssen:
A Simple Ant Colony Optimizer for Stochastic Shortest Path Problems. Algorithmica 64(4): 643-672 (2012) - [j10]Dirk Sudholt, Christian Thyssen:
Running time analysis of Ant Colony Optimization for shortest path problems. J. Discrete Algorithms 10: 165-180 (2012) - [c35]Alberto Moraglio, Dirk Sudholt:
Runtime analysis of convex evolutionary search. GECCO 2012: 649-656 - [c34]Dirk Sudholt:
Crossover speeds up building-block assembly. GECCO 2012: 689-702 - [c33]Dirk Sudholt:
Theory of swarm intelligence. GECCO (Companion) 2012: 1215-1238 - [c32]Leandro L. Minku, Dirk Sudholt, Xin Yao:
Evolutionary algorithms for the project scheduling problem: runtime analysis and improved design. GECCO 2012: 1221-1228 - [c31]Jonathan E. Rowe, Dirk Sudholt:
The choice of the offspring population size in the (1, λ) EA. GECCO 2012: 1349-1356 - [c30]Andrea Mambrini, Dirk Sudholt, Xin Yao:
Homogeneous and Heterogeneous Island Models for the Set Cover Problem. PPSN (1) 2012: 11-20 - [p3]Dirk Sudholt:
Parametrization and Balancing Local and Global Search. Handbook of Memetic Algorithms 2012: 55-72 - [i8]Jörg Lässig, Dirk Sudholt:
General Upper Bounds on the Running Time of Parallel Evolutionary Algorithms. CoRR abs/1206.3522 (2012) - 2011
- [j9]Dirk Sudholt:
Hybridizing Evolutionary Algorithms with Variable-Depth Search to Overcome Local Optima. Algorithmica 59(3): 343-368 (2011) - [j8]Benjamin Doerr, Frank Neumann, Dirk Sudholt, Carsten Witt:
Runtime analysis of the 1-ANT ant colony optimizer. Theor. Comput. Sci. 412(17): 1629-1644 (2011) - [c29]Dirk Sudholt:
Using markov-chain mixing time estimates for the analysis of ant colony optimization. FOGA 2011: 139-150 - [c28]Jörg Lässig, Dirk Sudholt:
Adaptive population models for offspring populations and parallel evolutionary algorithms. FOGA 2011: 181-192 - [c27]Timo Kötzing, Frank Neumann, Dirk Sudholt, Markus Wagner:
Simple max-min ant systems and the optimization of linear pseudo-boolean functions. FOGA 2011: 209-218 - [c26]Timo Kötzing, Dirk Sudholt, Madeleine Theile:
How crossover helps in pseudo-boolean optimization. GECCO 2011: 989-996 - [c25]Dirk Sudholt:
Theory of swarm intelligence. GECCO (Companion) 2011: 1381-1410 - [c24]Frank Neumann, Pietro S. Oliveto, Günter Rudolph, Dirk Sudholt:
On the effectiveness of crossover for migration in parallel evolutionary algorithms. GECCO 2011: 1587-1594 - [c23]Jörg Lässig, Dirk Sudholt:
Analysis of Speedups in Parallel Evolutionary Algorithms for Combinatorial Optimization - (Extended Abstract). ISAAC 2011: 405-414 - [p2]Dirk Sudholt:
Memetic Evolutionary Algorithms. Theory of Randomized Search Heuristics 2011: 141-169 - [i7]Jörg Lässig, Dirk Sudholt:
Adaptive Population Models for Offspring Populations and Parallel Evolutionary Algorithms. CoRR abs/1102.0588 (2011) - [i6]Dirk Sudholt:
A New Method for Lower Bounds on the Running Time of Evolutionary Algorithms. CoRR abs/1109.1504 (2011) - [i5]Jörg Lässig, Dirk Sudholt:
Analysis of Speedups in Parallel Evolutionary Algorithms for Combinatorial Optimization. CoRR abs/1109.1766 (2011) - [i4]Dirk Sudholt:
Memetic Algorithms: Parametrization and Balancing Local and Global Search. CoRR abs/1109.6441 (2011) - 2010
- [j7]Thomas Jansen, Dirk Sudholt:
Analysis of an Asymmetric Mutation Operator. Evol. Comput. 18(1): 1-26 (2010) - [j6]Thomas Sauerwald, Dirk Sudholt:
A self-stabilizing algorithm for cut problems in synchronous networks. Theor. Comput. Sci. 411(14-15): 1599-1612 (2010) - [j5]Dirk Sudholt, Carsten Witt:
Runtime analysis of a binary particle swarm optimizer. Theor. Comput. Sci. 411(21): 2084-2100 (2010) - [c22]Frank Neumann, Dirk Sudholt, Carsten Witt:
A few ants are enough: ACO with iteration-best update. GECCO 2010: 63-70 - [c21]Jörg Lässig, Dirk Sudholt:
The benefit of migration in parallel evolutionary algorithms. GECCO 2010: 1105-1112 - [c20]Christian Horoba, Dirk Sudholt:
Ant colony optimization for stochastic shortest path problems. GECCO 2010: 1465-1472 - [c19]Dirk Sudholt, Christine Zarges:
Analysis of an Iterated Local Search Algorithm for Vertex Coloring. ISAAC (1) 2010: 340-352 - [c18]Benjamin Doerr, Thomas Jansen, Dirk Sudholt, Carola Winzen, Christine Zarges:
Optimizing Monotone Functions Can Be Difficult. PPSN (1) 2010: 42-51 - [c17]Dirk Sudholt:
General Lower Bounds for the Running Time of Evolutionary Algorithms. PPSN (1) 2010: 124-133 - [c16]Jörg Lässig, Dirk Sudholt:
Experimental Supplements to the Theoretical Analysis of Migration in the Island Model. PPSN (1) 2010: 224-233 - [c15]Jörg Lässig, Dirk Sudholt:
General Scheme for Analyzing Running Times of Parallel Evolutionary Algorithms. PPSN (1) 2010: 234-243 - [i3]Timo Kötzing, Frank Neumann, Dirk Sudholt, Markus Wagner:
Simple Max-Min Ant Systems and the Optimization of Linear Pseudo-Boolean Functions. CoRR abs/1007.4707 (2010) - [i2]Benjamin Doerr, Thomas Jansen, Dirk Sudholt, Carola Winzen, Christine Zarges:
Optimizing Monotone Functions Can Be Difficult. CoRR abs/1010.1429 (2010)
2000 – 2009
- 2009
- [j4]Thomas Jansen, Melanie Schmidt, Dirk Sudholt, Carsten Witt, Christine Zarges:
Ingo Wegener. Evol. Comput. 17(1): 1-2 (2009) - [j3]Tobias Friedrich, Pietro S. Oliveto, Dirk Sudholt, Carsten Witt:
Analysis of Diversity-Preserving Mechanisms for Global Exploration. Evol. Comput. 17(4): 455-476 (2009) - [j2]Frank Neumann, Dirk Sudholt, Carsten Witt:
Analysis of different MMAS ACO algorithms on unimodal functions and plateaus. Swarm Intell. 3(1): 35-68 (2009) - [j1]Dirk Sudholt:
The impact of parametrization in memetic evolutionary algorithms. Theor. Comput. Sci. 410(26): 2511-2528 (2009) - [c14]Christian Horoba, Dirk Sudholt:
Running Time Analysis of ACO Systems for Shortest Path Problems. SLS 2009: 76-91 - [p1]Frank Neumann, Dirk Sudholt, Carsten Witt:
Computational Complexity of Ant Colony Optimization and Its Hybridization with Local Search. Innovations in Swarm Intelligence 2009: 91-120 - 2008
- [b1]Dirk Sudholt:
Computational complexity of evolutionary algorithms, hybridizations, and swarm intelligence. Dortmund University of Technology, 2008 - [c13]Frank Neumann, Dirk Sudholt, Carsten Witt:
Rigorous Analyses for the Combination of Ant Colony Optimization and Local Search. ANTS Conference 2008: 132-143 - [c12]Dirk Sudholt, Carsten Witt:
Runtime analysis of binary PSO. GECCO 2008: 135-142 - [c11]Dirk Sudholt:
Memetic algorithms with variable-depth search to overcome local optima. GECCO 2008: 787-794 - [c10]Tobias Friedrich, Pietro S. Oliveto, Dirk Sudholt, Carsten Witt:
Theoretical analysis of diversity mechanisms for global exploration. GECCO 2008: 945-952 - [c9]Thomas Sauerwald, Dirk Sudholt:
Self-stabilizing Cuts in Synchronous Networks. SIROCCO 2008: 234-246 - [i1]Dirk Sudholt, Carsten Witt:
Runtime Analysis of Binary PSO. Theory of Evolutionary Algorithms 2008 - 2007
- [c8]Benjamin Doerr, Frank Neumann, Dirk Sudholt, Carsten Witt:
On the runtime analysis of the 1-ANT ACO algorithm. GECCO 2007: 33-40 - [c7]Frank Neumann, Dirk Sudholt, Carsten Witt:
Comparing Variants of MMAS ACO Algorithms on Pseudo-Boolean Functions. SLS 2007: 61-75 - 2006
- [c6]Dirk Sudholt:
On the analysis of the (1+1) memetic algorithm. GECCO 2006: 493-500 - [c5]Dirk Sudholt:
Local Search in Evolutionary Algorithms: The Impact of the Local Search Frequency. ISAAC 2006: 359-368 - 2005
- [c4]Thomas Jansen, Dirk Sudholt:
Design and analysis of an asymmetric mutation operator. Congress on Evolutionary Computation 2005: 190-197 - [c3]Dirk Sudholt:
Crossover is provably essential for the ising model on trees. GECCO 2005: 1161-1167 - 2004
- [c2]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, Ingo Wegener:
Experimental Supplements to the Theoretical Analysis of EAs on Problems from Combinatorial Optimization. PPSN 2004: 21-30 - [c1]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, Ingo Wegener:
The Ising Model: Simple Evolutionary Algorithms as Adaptation Schemes. PPSN 2004: 31-40
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-12-10 20:51 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
