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ORCIDDirk Sudholt
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- affiliation: University of Passau, Germany
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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]