Michael Emmerich
Michael Emmerich
Associate Professor, LIACS, Leiden University & Lead AI Scientist at SILO.AI
Verified email at - Homepage
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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
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
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
A tutorial on multiobjective optimization: fundamentals and evolutionary methods
M Emmerich, AH Deutz
Natural computing 17 (3), 585-609, 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
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
On expected-improvement criteria for model-based multi-objective optimization
T Wagner, M Emmerich, A Deutz, W Ponweiser
International Conference on Parallel Problem Solving from Nature, 718-727, 2010
Single-and multi-objective evolutionary design optimization assisted by gaussian random field metamodels
M Emmerich
Dortmund, Univ., Diss., 2005, 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
Enhancing decision space diversity in evolutionary multiobjective algorithms
OM Shir, M Preuss, B Naujoks, M Emmerich
International Conference on Evolutionary Multi-Criterion Optimization, 95-109, 2009
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
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
Test problems based on Lamé superspheres
M Emmerich, AH Deutz
International Conference on Evolutionary Multi-Criterion Optimization, 922-936, 2007
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
Mixed-integer evolution strategy for chemical plant optimization with simulators
M Emmerich, M Grötzner, B Groß, M Schütz
Evolutionary Design and Manufacture, 55-67, 2000
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
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
Faster exact algorithms for computing expected hypervolume improvement
I Hupkens, A Deutz, K Yang, M Emmerich
international conference on evolutionary multi-criterion optimization, 65-79, 2015
Gradient-based/evolutionary relay hybrid for computing Pareto front approximations maximizing the S-metric
M Emmerich, A Deutz, N Beume
International Workshop on Hybrid Metaheuristics, 140-156, 2007
Design of graph-based evolutionary algorithms: A case study for chemical process networks
M Emmerich, M Grötzner, M Schütz
Evolutionary Computation 9 (3), 329-354, 2001
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