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
Tobias FriedrichChair for Algorithm Engineering, Hasso Plattner Institute, Potsdam, GermanyVerified email at hpi.de
Per Kristian LehreProfessor at University 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
Martin S. KrejcaLIX, CNRS, Ecole Polytechnique, IPPVerified email at polytechnique.edu
Barbora TrubenováETH ZurichVerified email at env.ethz.ch
Edgar Covantes OsunaProfessorVerified email at tec.mx
Benjamin DoerrProfessor at Ecole Polytechnique, FranceVerified email at lix.polytechnique.fr
Carola DoerrCNRS research director (DR) at Sorbonne Université, Paris, FranceVerified email at mpi-inf.mpg.de
Dogan CorusKadir Has UniversityVerified email at khas.edu.tr
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 and University of Birmingham (UK)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	142	2012
Analysis of diversity-preserving mechanisms for global exploration T Friedrich, PS Oliveto, D Sudholt, C Witt Evolutionary Computation 17 (4), 455-476, 2009	110	2009
Crossover is provably essential for the Ising model on trees D Sudholt Proceedings of the 7th annual conference on Genetic and evolutionary …, 2005	109	2005
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	100	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	91	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	84	2011
Parallel evolutionary algorithms D Sudholt Springer Handbook of Computational Intelligence, 929-959, 2015	80	2015
Running time analysis of ant colony optimization for shortest path problems D Sudholt, C Thyssen Journal of Discrete Algorithms 10, 165-180, 2012	77	2012
Analysis of different MMAS ACO algorithms on unimodal functions and plateaus F Neumann, D Sudholt, C Witt Swarm Intelligence 3, 35-68, 2009	77	2009
The impact of parametrization in memetic evolutionary algorithms D Sudholt Theoretical Computer Science 410 (26), 2511-2528, 2009	75	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	74	2011
Unbiased black-box complexity of parallel search G Badkobeh, PK Lehre, D Sudholt Parallel Problem Solving from Nature–PPSN XIII: 13th International …, 2014	71	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	69	2013
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	60	2016
How crossover speeds up building block assembly in genetic algorithms D Sudholt Evolutionary computation 25 (2), 237-274, 2017	57	2017
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	57	2007
Crossover speeds up building-block assembly D Sudholt Proceedings of the 14th annual conference on Genetic and evolutionary …, 2012	55	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	53	2013
A simple ant colony optimizer for stochastic shortest path problems D Sudholt, C Thyssen Algorithmica 64, 643-672, 2012	53	2012
The benefits of population diversity in evolutionary algorithms: a survey of rigorous runtime analyses D Sudholt Theory of evolutionary computation: Recent developments in discrete …, 2020	52	2020

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