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Michael T.M. Emmerich (Dr. rer. nat.)
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
Title
Cited by
Cited by
Year
SMS-EMOA: Multiobjective selection based on dominated hypervolume
N Beume, B Naujoks, M Emmerich
European Journal of Operational Research 181 (3), 1653-1669, 2007
20952007
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
7722005
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
7582006
A tutorial on multiobjective optimization: fundamentals and evolutionary methods
MTM Emmerich, AH Deutz
Natural computing 17, 585-609, 2018
7052018
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
3132002
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
2482011
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
1642010
Single-and multi-objective evolutionary design optimization assisted by gaussian random field metamodels
M Emmerich
University of Dormund, 2005
1362005
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
1302008
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
1222019
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
1202009
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
1112013
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
1052005
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
1022010
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
952017
Test problems based on Lamé superspheres
MTM Emmerich, AH Deutz
Evolutionary Multi-Criterion Optimization: 4th International Conference, EMO …, 2007
912007
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
832012
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
832000
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
822017
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
802016
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