Dirk Sudholt

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Pietro S. OlivetoProfessor of Computer Science, Chair in Algorithms, The University of SheffieldVerifierad e-postadress på sheffield.ac.uk
Carsten WittTechnical University of DenmarkVerifierad e-postadress på imm.dtu.dk
Frank NeumannOptimisation and Logistics, School of ComputerVerifierad e-postadress på adelaide.edu.au
Duc-Cuong DangUniversity of Southampton, UKVerifierad e-postadress på soton.ac.uk
Tobias FriedrichChair for Algorithm Engineering, Hasso Plattner Institute, Potsdam, GermanyVerifierad e-postadress på hpi.de
Per Kristian LehreProfessor at University of Birmingham and UKRI Turing AI Acceleration FellowVerifierad e-postadress på bham.ac.uk
Thomas JansenAberystwyth UniversityVerifierad e-postadress på aber.ac.uk
Tiago PaixaoInstituto Gulbenkian de CiênciaVerifierad e-postadress på igc.gulbenkian.pt
Andrew M. SuttonUniversity of Minnesota DuluthVerifierad e-postadress på umn.edu
Christine ZargesDepartment of Computer Science, Aberystwyth UniversityVerifierad e-postadress på aber.ac.uk
Martin S. KrejcaLIX, CNRS, Ecole Polytechnique, IPPVerifierad e-postadress på polytechnique.edu
Andre OprisAssistant Professor, University of PassauVerifierad e-postadress på uni-passau.de
Edgar Covantes OsunaData ScientistVerifierad e-postadress på heineken.com
Barbora TrubenováETH ZurichVerifierad e-postadress på env.ethz.ch
Mario Alejandro Hevia FajardoResearch Fellow, University of BirminghamVerifierad e-postadress på bham.ac.uk
Jakob BossekPaderborn UniversityVerifierad e-postadress på uni-paderborn.de
Benjamin DoerrProfessor at Ecole Polytechnique, FranceVerifierad e-postadress på lix.polytechnique.fr
Johannes LenglerETH ZürichVerifierad e-postadress på inf.ethz.ch
Golnaz BadkobehCity, University of LondonVerifierad e-postadress på city.ac.uk
Carola DoerrCNRS research director (DR) at Sorbonne Université, Paris, FranceVerifierad e-postadress på mpi-inf.mpg.de

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Dirk Sudholt

University of Passau, Germany

Verifierad e-postadress på uni-passau.de - Startsida

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


Titel Sortera efter citat Sortera efter år Sortera efter titel	Citeras av Citeras av	År
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	172	2012
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	145	2017
Analysis of diversity-preserving mechanisms for global exploration T Friedrich, PS Oliveto, D Sudholt, C Witt Evolutionary Computation 17 (4), 455-476, 2009	129	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	115	2014
Crossover is provably essential for the Ising model on trees D Sudholt Proceedings of the 7th annual conference on Genetic and evolutionary …, 2005	114	2005
Parallel evolutionary algorithms D Sudholt Springer Handbook of Computational Intelligence, 929-959, 2015	104	2015
How crossover helps in pseudo-Boolean optimization T Kötzing, D Sudholt, M Theile Proceedings of the 13th annual conference on Genetic and evolutionary …, 2011	98	2011
Running time analysis of ant colony optimization for shortest path problems D Sudholt, C Thyssen Journal of Discrete Algorithms 10, 165-180, 2012	94	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	88	2020
Analysis of different MMAS ACO algorithms on unimodal functions and plateaus F Neumann, D Sudholt, C Witt Swarm Intelligence 3, 35-68, 2009	85	2009
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	83	2013
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	82	2011
The impact of parametrization in memetic evolutionary algorithms D Sudholt Theoretical Computer Science 410 (26), 2511-2528, 2009	82	2009
Unbiased black-box complexity of parallel search G Badkobeh, PK Lehre, D Sudholt Parallel Problem Solving from Nature–PPSN XIII: 13th International …, 2014	79	2014
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	76	2016
How crossover speeds up building block assembly in genetic algorithms D Sudholt Evolutionary computation 25 (2), 237-274, 2017	73	2017
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	64	2013
A simple ant colony optimizer for stochastic shortest path problems D Sudholt, C Thyssen Algorithmica 64, 643-672, 2012	60	2012
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	58	2007
Crossover speeds up building-block assembly D Sudholt Proceedings of the 14th annual conference on Genetic and evolutionary …, 2012	57	2012

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