Michael T.M. Emmerich (Dr. rer. nat.)

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Thomas BäckProfessor of Computer Science, Leiden University; Chief Scientist, NORCE Research Centre, NorwayVerified email at liacs.leidenuniv.nl
Boris NaujoksCologne University of Applied SciencesVerified email at fh-koeln.de
Hao WangAssistant Professor in Leiden UniversityVerified email at liacs.leidenuniv.nl
Iryna YevseyevaDe Montfort University, LeicesterVerified email at dmu.ac.uk
Kaifeng YangPostdoc, University of Applied Sciences Upper AustriaVerified email at fh-hagenberg.at
Vitor Basto-FernandesISCTE-IULVerified email at iscte.pt
Mike PreussUniversiteit LeidenVerified email at liacs.leidenuniv.nl
Johannes KruisselbrinkBiometris, Wageningen University & ResearchVerified email at wur.nl
Koen van der BlomPostdoc, LIP6, Sorbonne UniversitéVerified email at sorbonne-universite.fr
Ofer M. ShirAssociate Professor of Computer Science, Tel-Hai College, Upper Galilee, IsraelVerified email at alumni.Princeton.EDU
Andreas BenderProfessor for Molecular Informatics in CambridgeVerified email at cam.ac.uk
Longmei LiLeiden Institute of Advanced Computer ScienceVerified email at liacs.leidenuniv.nl
Eelke van der HorstThe HyveVerified email at thehyve.nl
Heike TrautmannProfessor of Machine Learning and Optimisation, Paderborn University, GermanyVerified email at uni-paderborn.de
Jouke DijkstraAssociate professor, section leader vascular and molecular imaging, Leiden University Medical CenterVerified email at lumc.nl
Niki van SteinLeiden UniversityVerified email at liacs.leidenuniv.nl
Zhiwei YangNational University of Defence TehnologyVerified email at liacs.leidenuniv.nl
Carlos M. FonsecaDepartment of Informatics Engineering, University of CoimbraVerified email at dei.uc.pt
Aske PlaatLeiden UniversityVerified email at liacs.leidenuniv.nl
Hui WangPostdoc fellow, Paris University Dauphine-PSLVerified email at dauphine.psl.eu

Michael T.M. Emmerich (Dr. rer. nat.)

Lead AI Scientist Silo.ai & Jyväskylä University, Finland & LIACS Leiden Univ. NL

Verified email at liacs.nl - Homepage

Multicriteria Optimization Complex Networks Gaussian Processes Sustainable Design


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SMS-EMOA: Multiobjective selection based on dominated hypervolume N Beume, B Naujoks, M Emmerich European Journal of Operational Research 181 (3), 1653-1669, 2007	2008	2007
An EMO algorithm using the hypervolume measure as selection criterion M Emmerich, N Beume, B Naujoks International Conference on Evolutionary Multi-Criterion Optimization, 62-76, 2005	751	2005
Single-and multiobjective evolutionary optimization assisted by Gaussian random field metamodels MTM Emmerich, KC Giannakoglou, B Naujoks IEEE Transactions on Evolutionary Computation 10 (4), 421-439, 2006	710	2006
A tutorial on multiobjective optimization: fundamentals and evolutionary methods MTM Emmerich, AH Deutz Natural computing 17, 585-609, 2018	607	2018
Metamodel—Assisted evolution strategies M Emmerich, A Giotis, M Özdemir, T Bäck, K Giannakoglou International Conference on parallel problem solving from nature, 361-370, 2002	305	2002
Hypervolume-based expected improvement: Monotonicity properties and exact computation MTM Emmerich, AH Deutz, JW Klinkenberg 2011 IEEE Congress of Evolutionary Computation (CEC), 2147-2154, 2011	231	2011
On expected-improvement criteria for model-based multi-objective optimization T Wagner, M Emmerich, A Deutz, W Ponweiser Parallel Problem Solving from Nature, PPSN XI: 11th International Conference …, 2010	159	2010
Single-and multi-objective evolutionary design optimization assisted by gaussian random field metamodels M Emmerich University of Dormund, 2005	136	2005
The computation of the expected improvement in dominated hypervolume of Pareto front approximations M Emmerich, J Klinkenberg Rapport technique, Leiden University 34, 7-3, 2008	126	2008
Enhancing decision space diversity in evolutionary multiobjective algorithms OM Shir, M Preuss, B Naujoks, M Emmerich Evolutionary Multi-Criterion Optimization: 5th International Conference, EMO …, 2009	119	2009
Multi-objective Bayesian global optimization using expected hypervolume improvement gradient K Yang, M Emmerich, A Deutz, T Bäck Swarm and evolutionary computation 44, 945-956, 2019	106	2019
Multi-objective optimisation using S-metric selection: Application to three-dimensional solution spaces B Naujoks, N Beume, M Emmerich 2005 IEEE Congress on Evolutionary Computation 2, 1282-1289, 2005	105	2005
Mixed integer evolution strategies for parameter optimization R Li, MTM Emmerich, J Eggermont, T Bäck, M Schütz, J Dijkstra, ... Evolutionary computation 21 (1), 29-64, 2013	104	2013
Adaptive niche radii and niche shapes approaches for niching with the CMA-ES OM Shir, M Emmerich, T Bäck Evolutionary computation 18 (1), 97-126, 2010	100	2010
Test problems based on Lamé superspheres MTM Emmerich, AH Deutz Evolutionary Multi-Criterion Optimization: 4th International Conference, EMO …, 2007	89	2007
Surrogate‐assisted multicriteria optimization: Complexities, prospective solutions, and business case R Allmendinger, MTM Emmerich, J Hakanen, Y Jin, E Rigoni Journal of Multi‐Criteria Decision Analysis 24 (1-2), 5-24, 2017	86	2017
Mixed-integer evolution strategy for chemical plant optimization with simulators M Emmerich, M Grötzner, B Groß, M Schütz Evolutionary Design and Manufacture: Selected Papers from ACDM’00, 55-67, 2000	81	2000
A new acquisition function for Bayesian optimization based on the moment-generating function H Wang, B van Stein, M Emmerich, T Back 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC …, 2017	78	2017
Robust multi-criteria design optimisation in building design CJ Hopfe, MTM Emmerich, R Marijt, J Hensen Proceedings of building simulation and optimization, Loughborough, UK, 118-125, 2012	77	2012
A multicriteria generalization of Bayesian global optimization M Emmerich, K Yang, A Deutz, H Wang, CM Fonseca Advances in stochastic and deterministic global optimization, 229-242, 2016	74	2016

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