Dirk Sudholt
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Zitiert von
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A new method for lower bounds on the running time of evolutionary algorithms
D Sudholt
IEEE Transactions on Evolutionary Computation 17 (3), 418-435, 2012
1132012
Crossover is provably essential for the Ising model on trees
D Sudholt
Proceedings of the 7th annual conference on Genetic and evolutionary …, 2005
952005
Analysis of diversity-preserving mechanisms for global exploration
T Friedrich, PS Oliveto, D Sudholt, C Witt
Evolutionary Computation 17 (4), 455-476, 2009
932009
The impact of parametrization in memetic evolutionary algorithms
D Sudholt
Theoretical Computer Science 410 (26), 2511-2528, 2009
752009
The choice of the offspring population size in the (1, λ) evolutionary algorithm
JE Rowe, D Sudholt
Theoretical Computer Science 545, 20-38, 2014
712014
How crossover helps in pseudo-boolean optimization
T Kötzing, D Sudholt, M Theile
Proceedings of the 13th annual conference on Genetic and evolutionary …, 2011
702011
Analysis of different MMAS ACO algorithms on unimodal functions and plateaus
F Neumann, D Sudholt, C Witt
Swarm Intelligence 3 (1), 35-68, 2009
692009
Adaptive population models for offspring populations and parallel evolutionary algorithms
J Lässig, D Sudholt
Proceedings of the 11th workshop proceedings on Foundations of genetic …, 2011
632011
On the runtime analysis of the 1-ANT ACO algorithm
B Doerr, F Neumann, D Sudholt, C Witt
Proceedings of the 9th annual conference on Genetic and evolutionary …, 2007
552007
Mutation rate matters even when optimizing monotonic functions
B Doerr, T Jansen, D Sudholt, C Winzen, C Zarges
Evolutionary computation 21 (1), 1-27, 2013
542013
Running time analysis of ant colony optimization for shortest path problems
D Sudholt, C Thyssen
Journal of Discrete Algorithms 10, 165-180, 2012
532012
Crossover speeds up building-block assembly
D Sudholt
Proceedings of the 14th annual conference on Genetic and evolutionary …, 2012
512012
Parallel evolutionary algorithms
D Sudholt
Springer Handbook of Computational Intelligence, 929-959, 2015
502015
Unbiased black-box complexity of parallel search
G Badkobeh, PK Lehre, D Sudholt
International Conference on Parallel Problem Solving from Nature, 892-901, 2014
502014
Escaping local optima using crossover with emergent diversity
DC Dang, T Friedrich, T Kötzing, MS Krejca, PK Lehre, PS Oliveto, ...
IEEE Transactions on Evolutionary Computation 22 (3), 484-497, 2017
472017
The choice of the offspring population size in the (1, λ) EA
JE Rowe, D Sudholt
Proceedings of the 14th annual conference on Genetic and evolutionary …, 2012
472012
A simple ant colony optimizer for stochastic shortest path problems
D Sudholt, C Thyssen
Algorithmica 64 (4), 643-672, 2012
442012
Rigorous analyses for the combination of ant colony optimization and local search
F Neumann, D Sudholt, C Witt
International Conference on Ant Colony Optimization and Swarm Intelligence …, 2008
442008
Improved evolutionary algorithm design for the project scheduling problem based on runtime analysis
LL Minku, D Sudholt, X Yao
IEEE Transactions on Software Engineering 40 (1), 83-102, 2013
402013
Design and analysis of migration in parallel evolutionary algorithms
J Lässig, D Sudholt
Soft Computing 17 (7), 1121-1144, 2013
402013
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