Dirk Sudholt

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Carsten WittTechnical University of DenmarkVerified email at imm.dtu.dk
Pietro S. OlivetoSenior Lecturer, EPSRC Early Career Fellow, The University of Sheffield, UKVerified email at sheffield.ac.uk
Frank NeumannOptimisation and Logistics, School of ComputerVerified email at adelaide.edu.au
Tobias FriedrichChair for Algorithm Engineering, Hasso Plattner Institute, Potsdam, GermanyVerified email at hpi.de
Per Kristian LehreUniversity of Birmingham and UKRI Turing AI Acceleration FellowVerified email at cs.bham.ac.uk
Thomas JansenAberystwyth UniversityVerified email at aber.ac.uk
Duc-Cuong DangUniversity of Southampton, UKVerified email at soton.ac.uk
Tiago PaixaoInstituto Gulbenkian de CiênciaVerified email at igc.gulbenkian.pt
Christine ZargesDepartment of Computer Science, Aberystwyth UniversityVerified email at aber.ac.uk
Andrew M. SuttonUniversity of Minnesota DuluthVerified email at umn.edu
Edgar Covantes OsunaProfessorVerified email at tec.mx
Martin S. KrejcaLIP6, CNRS, Sorbonne UniversityVerified email at lip6.fr
Barbora TrubenováETH ZurichVerified email at env.ethz.ch
Benjamin DoerrProfessor at Ecole Polytechnique, France; senior researcher at Max Planck Institute for InformaticsVerified email at mpi-inf.mpg.de
Dogan CorusKadir Has UniversityVerified email at khas.edu.tr
Carola DoerrCNRS researcher at Sorbonne Université, Paris, FranceVerified email at mpi-inf.mpg.de
Golnaz BadkobehThe University of WarwickVerified email at warwick.ac.uk
Xin YaoSouthern University of Science and Technology (SUSTech) and University of BirminghamVerified email at cs.bham.ac.uk
Jonathan RoweSchool of Computer Science, University of Birmingham Verified email at cs.bham.ac.uk
Sam NallaperumaUniversity of CambridgeVerified email at cam.ac.uk

Dirk Sudholt

University of Passau, Germany

Verified email at uni-passau.de - Homepage

Theory of nature-inspired algorithms randomised algorithms natural computation evolutionary computation


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	129	2012
Crossover is provably essential for the Ising model on trees D Sudholt Proceedings of the 7th annual conference on Genetic and evolutionary …, 2005	105	2005
Analysis of diversity-preserving mechanisms for global exploration T Friedrich, PS Oliveto, D Sudholt, C Witt Evolutionary Computation 17 (4), 455-476, 2009	100	2009
The choice of the offspring population size in the (1, λ) evolutionary algorithm JE Rowe, D Sudholt Theoretical Computer Science 545, 20-38, 2014	81	2014
The impact of parametrization in memetic evolutionary algorithms D Sudholt Theoretical Computer Science 410 (26), 2511-2528, 2009	76	2009
Analysis of different MMAS ACO algorithms on unimodal functions and plateaus F Neumann, D Sudholt, C Witt Swarm Intelligence 3 (1), 35-68, 2009	76	2009
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	73	2017
How crossover helps in pseudo-Boolean optimization T Kötzing, D Sudholt, M Theile Proceedings of the 13th annual conference on Genetic and evolutionary …, 2011	73	2011
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	69	2011
Running time analysis of ant colony optimization for shortest path problems D Sudholt, C Thyssen Journal of Discrete Algorithms 10, 165-180, 2012	68	2012
Unbiased black-box complexity of parallel search G Badkobeh, PK Lehre, D Sudholt International Conference on Parallel Problem Solving from Nature, 892-901, 2014	64	2014
Parallel evolutionary algorithms D Sudholt Springer handbook of computational intelligence, 929-959, 2015	61	2015
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	61	2013
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	56	2007
Crossover speeds up building-block assembly D Sudholt Proceedings of the 14th annual conference on Genetic and evolutionary …, 2012	52	2012
A simple ant colony optimizer for stochastic shortest path problems D Sudholt, C Thyssen Algorithmica 64 (4), 643-672, 2012	50	2012
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	48	2013
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	48	2012
How crossover speeds up building block assembly in genetic algorithms D Sudholt Evolutionary computation 25 (2), 237-274, 2017	47	2017
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	45	2008

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