Dirk Sudholt

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Carsten WittTechnical University of DenmarkVerified email at imm.dtu.dk
Pietro S. OlivetoProfessor of Computer Science, Chair in Algorithms, The University of SheffieldVerified email at sheffield.ac.uk
Frank NeumannOptimisation and Logistics, School of ComputerVerified email at adelaide.edu.au
Per Kristian LehreUniversity of Birmingham and UKRI Turing AI Acceleration FellowVerified email at cs.bham.ac.uk
Tobias FriedrichChair for Algorithm Engineering, Hasso Plattner Institute, Potsdam, GermanyVerified email at hpi.de
Thomas JansenAberystwyth UniversityVerified email at aber.ac.uk
Duc-Cuong DangUniversity of Southampton, UKVerified email at soton.ac.uk
Christine ZargesDepartment of Computer Science, Aberystwyth UniversityVerified email at aber.ac.uk
Tiago PaixaoInstituto Gulbenkian de CiênciaVerified email at igc.gulbenkian.pt
Andrew M. SuttonUniversity of Minnesota DuluthVerified email at umn.edu
Martin S. KrejcaLIP6, CNRS, Sorbonne UniversityVerified email at lip6.fr
Edgar Covantes OsunaProfessorVerified email at tec.mx
Barbora TrubenováETH ZurichVerified email at env.ethz.ch
Benjamin DoerrProfessor at Ecole Polytechnique, FranceVerified email at lix.polytechnique.fr
Dogan CorusKadir Has UniversityVerified email at khas.edu.tr
Carola DoerrCNRS researcher at Sorbonne Université, Paris, FranceVerified email at mpi-inf.mpg.de
Golnaz BadkobehGoldsmiths, University of LondonVerified email at gold.ac.uk
Heiko RöglinProfessor of Computer Science, University of BonnVerified email at cs.uni-bonn.de
Xin YaoSouthern University of Science and Technology, University of Birmingham (UK) and NORCE (Norway)Verified email at cs.bham.ac.uk
Jonathan RoweSchool of Computer Science, University of Birmingham Verified email at cs.bham.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


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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	134	2012
Crossover is provably essential for the Ising model on trees D Sudholt Proceedings of the 7th annual conference on Genetic and evolutionary …, 2005	108	2005
Analysis of diversity-preserving mechanisms for global exploration T Friedrich, PS Oliveto, D Sudholt, C Witt Evolutionary Computation 17 (4), 455-476, 2009	103	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	88	2017
The choice of the offspring population size in the (1, λ) evolutionary algorithm JE Rowe, D Sudholt Theoretical Computer Science 545, 20-38, 2014	86	2014
How crossover helps in pseudo-Boolean optimization T Kötzing, D Sudholt, M Theile Proceedings of the 13th annual conference on Genetic and evolutionary …, 2011	78	2011
Analysis of different MMAS ACO algorithms on unimodal functions and plateaus F Neumann, D Sudholt, C Witt Swarm Intelligence 3 (1), 35-68, 2009	77	2009
The impact of parametrization in memetic evolutionary algorithms D Sudholt Theoretical Computer Science 410 (26), 2511-2528, 2009	74	2009
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	73	2011
Parallel evolutionary algorithms D Sudholt Springer Handbook of Computational Intelligence, 929-959, 2015	71	2015
Running time analysis of ant colony optimization for shortest path problems D Sudholt, C Thyssen Journal of Discrete Algorithms 10, 165-180, 2012	71	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	69	2014
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	64	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
How crossover speeds up building block assembly in genetic algorithms D Sudholt Evolutionary computation 25 (2), 237-274, 2017	55	2017
Crossover speeds up building-block assembly D Sudholt Proceedings of the 14th annual conference on Genetic and evolutionary …, 2012	54	2012
Escaping local optima with diversity mechanisms and crossover DC Dang, T Friedrich, T Kötzing, MS Krejca, PK Lehre, PS Oliveto, ... Proceedings of the Genetic and Evolutionary Computation Conference 2016, 645-652, 2016	52	2016
A simple ant colony optimizer for stochastic shortest path problems D Sudholt, C Thyssen Algorithmica 64 (4), 643-672, 2012	52	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	50	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

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