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ORCIDAndré H. Deutz
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2020 – today
- 2024
[i8]Hao Wang, Angel E. Rodriguez-Fernandez, Lourdes Uribe, André H. Deutz, Oziel Cortés-Piña, Oliver Schütze:
A Newton Method for Hausdorff Approximations of the Pareto Front within Multi-objective Evolutionary Algorithms. CoRR abs/2405.05721 (2024)- [i7]Michael T. M. Emmerich, André H. Deutz:
Multicriteria Optimization and Decision Making: Principles, Algorithms and Case Studies. CoRR abs/2407.00359 (2024) - [i6]Ksenia Pereverdieva, André H. Deutz, Tessa Ezendam, Thomas Bäck, Hèrm Hofmeyer, Michael T. M. Emmerich:
Comparative Analysis of Indicators for Multiobjective Diversity Optimization. CoRR abs/2410.18900 (2024) - 2023
- [j11]Michael Emmerich, André H. Deutz, Iryna Yevseyeva:
Preface. Nat. Comput. 22(2): 225-226 (2023) - [c29]André H. Deutz
, Michael Emmerich, Hao Wang:
The Hypervolume Indicator Hessian Matrix: Analytical Expression, Computational Time Complexity, and Sparsity. EMO 2023: 405-418 - [c28]Ksenia Pereverdieva, Michael Emmerich, André H. Deutz, Tessa Ezendam, Thomas Bäck, Hèrm Hofmeyer:
The Prism-Net Search Space Representation for Multi-objective Building Spatial Design. EMO 2023: 476-489 - [p5]André H. Deutz, Michael Emmerich, Yali Wang:
Many-Criteria Dominance Relations. Many-Criteria Optimization and Decision Analysis 2023: 81-111 - [p4]Tinkle Chugh, António Gaspar-Cunha, André H. Deutz, João A. Duro, Daniel C. Oara, Alma A. M. Rahat:
Identifying Correlations in Understanding and Solving Many-Objective Optimisation Problems. Many-Criteria Optimization and Decision Analysis 2023: 241-267 - [e3]Michael Emmerich, André H. Deutz, Hao Wang, Anna V. Kononova, Boris Naujoks, Ke Li, Kaisa Miettinen, Iryna Yevseyeva:
Evolutionary Multi-Criterion Optimization - 12th International Conference, EMO 2023, Leiden, The Netherlands, March 20-24, 2023, Proceedings. Lecture Notes in Computer Science 13970, Springer 2023, ISBN 978-3-031-27249-3 [contents] - [d1]Hao Wang, Michael Emmerich, André H. Deutz, Víctor Adrián Sosa-Hernández, Oliver Schütze:
Experimental Results for the study "The Hypervolume Newton Method for Constrained Multi-Objective Optimization Problems". Zenodo, 2023
- 2022
- [i5]André H. Deutz, Michael T. M. Emmerich, Hao Wang:
The Hypervolume Indicator Hessian Matrix: Analytical Expression, Computational Time Complexity, and Sparsity. CoRR abs/2211.04171 (2022) - 2021
- [j10]Christian Grimme, Pascal Kerschke, Pelin Aspar, Heike Trautmann, Mike Preuss, André H. Deutz, Hao Wang, Michael Emmerich:
Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization. Comput. Oper. Res. 136: 105489 (2021) - [j9]André H. Deutz, Michael Emmerich, Yaroslav D. Sergeyev, Iryna Yevseyeva:
Preface to the special issue dedicated to the 14th international workshop on global optimization held in Leiden, The Netherlands, September 18-21, 2018. J. Glob. Optim. 79(2): 279-280 (2021) - 2020
- [j8]Víctor Adrián Sosa-Hernández, Oliver Schütze, Hao Wang, André H. Deutz, Michael Emmerich:
The Set-Based Hypervolume Newton Method for Bi-Objective Optimization. IEEE Trans. Cybern. 50(5): 2186-2196 (2020) - [c27]Yali Wang, André H. Deutz, Thomas Bäck, Michael Emmerich:
Improving Many-Objective Evolutionary Algorithms by Means of Edge-Rotated Cones. PPSN (2) 2020: 313-326 - [c26]Yali Wang, André H. Deutz, Thomas Bäck, Michael Emmerich:
Edge-Rotated Cone Orders in Multi-objective Evolutionary Algorithms for Improved Convergence and Preference Articulation. SSCI 2020: 165-172 - [p3]Michael T. M. Emmerich, Kaifeng Yang, André H. Deutz:
Infill Criteria for Multiobjective Bayesian Optimization. High-Performance Simulation-Based Optimization 2020: 3-16 - [e2]Thomas Bäck, Mike Preuss, André H. Deutz, Hao Wang, Carola Doerr, Michael T. M. Emmerich, Heike Trautmann:
Parallel Problem Solving from Nature - PPSN XVI - 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part I. Lecture Notes in Computer Science 12269, Springer 2020, ISBN 978-3-030-58111-4 [contents] - [e1]Thomas Bäck, Mike Preuss, André H. Deutz, Hao Wang, Carola Doerr, Michael T. M. Emmerich, Heike Trautmann:
Parallel Problem Solving from Nature - PPSN XVI - 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part II. Lecture Notes in Computer Science 12270, Springer 2020, ISBN 978-3-030-58114-5 [contents] - [i4]Yali Wang, André H. Deutz, Thomas Bäck, Michael T. M. Emmerich:
Improving Many-objective Evolutionary Algorithms by Means of Expanded Cone Orders. CoRR abs/2004.06941 (2020)
2010 – 2019
- 2019
- [j7]Pascal Kerschke, Hao Wang, Mike Preuss, Christian Grimme, André H. Deutz, Heike Trautmann, Michael T. M. Emmerich:
Search Dynamics on Multimodal Multiobjective Problems. Evol. Comput. 27(4): 577-609 (2019) - [j6]Iryna Yevseyeva, Eelke B. Lenselink, Alice de Vries, Adriaan P. IJzerman, André H. Deutz, Michael T. M. Emmerich:
Application of portfolio optimization to drug discovery. Inf. Sci. 475: 29-43 (2019) - [j5]Kaifeng Yang, Michael Emmerich, André H. Deutz, Thomas Bäck:
Efficient computation of expected hypervolume improvement using box decomposition algorithms. J. Glob. Optim. 75(1): 3-34 (2019) - [j4]Kaifeng Yang, Michael Emmerich, André H. Deutz, Thomas Bäck:
Multi-Objective Bayesian Global Optimization using expected hypervolume improvement gradient. Swarm Evol. Comput. 44: 945-956 (2019) - [c25]Yali Wang, Michael Emmerich, André H. Deutz, Thomas Bäck:
Diversity-Indicator Based Multi-Objective Evolutionary Algorithm: DI-MOEA. EMO 2019: 346-358 - [c24]André H. Deutz, Michael Emmerich, Kaifeng Yang:
The Expected R2-Indicator Improvement for Multi-objective Bayesian Optimization. EMO 2019: 359-370 - [i3]Kaifeng Yang, Michael Emmerich, André H. Deutz, Thomas Bäck:
Efficient Computation of Expected Hypervolume Improvement Using Box Decomposition Algorithms. CoRR abs/1904.12672 (2019) - [i2]Lieuwe Vinkhuijzen, André H. Deutz:
A Simple Proof of Vyalyi's Theorem and some Generalizations. Electron. Colloquium Comput. Complex. TR19 (2019) - 2018
- [j3]Michael T. M. Emmerich, André H. Deutz:
A tutorial on multiobjective optimization: fundamentals and evolutionary methods. Nat. Comput. 17(3): 585-609 (2018) - 2017
- [c23]Hao Wang, André H. Deutz, Thomas Bäck, Michael Emmerich:
Hypervolume Indicator Gradient Ascent Multi-objective Optimization. EMO 2017: 654-669 - [c22]Kaifeng Yang, Michael Emmerich, André H. Deutz, Carlos M. Fonseca:
Computing 3-D Expected Hypervolume Improvement and Related Integrals in Asymptotically Optimal Time. EMO 2017: 685-700 - 2016
- [c21]Kaifeng Yang, André H. Deutz, Zhiwei Yang, Thomas Bäck, Michael T. M. Emmerich:
Truncated expected hypervolume improvement: Exact computation and application. CEC 2016: 4350-4357 - [c20]Kaifeng Yang, Longmei Li, André H. Deutz, Thomas Bäck, Michael Emmerich:
Preference-based multiobjective optimization using truncated expected hypervolume improvement. ICNC-FSKD 2016: 276-281 - [c19]Pascal Kerschke, Hao Wang, Mike Preuss, Christian Grimme, André H. Deutz, Heike Trautmann, Michael Emmerich:
Towards Analyzing Multimodality of Continuous Multiobjective Landscapes. PPSN 2016: 962-972 - [p2]Michael Emmerich, Kaifeng Yang, André H. Deutz, Hao Wang, Carlos M. Fonseca:
A Multicriteria Generalization of Bayesian Global Optimization. Advances in Stochastic and Deterministic Global Optimization 2016: 229-242 - 2015
- [c18]Michael T. M. Emmerich, André H. Deutz, Iryna Yevseyeva:
A Bayesian Approach to Portfolio Selection in Multicriteria Group Decision Making. CENTERIS/ProjMAN/HCist 2015: 993-1000 - [c17]Iris Hupkens, André H. Deutz, Kaifeng Yang, Michael T. M. Emmerich:
Faster Exact Algorithms for Computing Expected Hypervolume Improvement. EMO (2) 2015: 65-79 - [c16]Wilco Verhoef, André H. Deutz, Michael T. M. Emmerich:
On Gradient-Based and Swarm-Based Algorithms for Set-Oriented Bicriteria Optimization. EVOLVE 2015: 18-36 - [c15]Asep Maulana, André H. Deutz, Erik A. Schultes, Michael T. M. Emmerich:
Community Detection in NK Landscapes - An Empirical Study of Complexity Transitions in Interactive Networks. EVOLVE 2015: 163-176 - [c14]Hao Wang, Yiyi Ren, André H. Deutz, Michael T. M. Emmerich:
On Steering Dominated Points in Hypervolume Indicator Gradient Ascent for Bi-Objective Optimization. NEO 2015: 175-203 - 2014
- [i1]Iris Hupkens, Michael T. M. Emmerich, André H. Deutz:
Faster Computation of Expected Hypervolume Improvement. CoRR abs/1408.7114 (2014) - 2013
- [c13]Michael Emmerich, André H. Deutz, Johannes W. Kruisselbrink, Pradyumn Kumar Shukla:
Cone-Based Hypervolume Indicators: Construction, Properties, and Efficient Computation. EMO 2013: 111-127 - [c12]Pradyumn Kumar Shukla, Michael Emmerich, André H. Deutz:
A Theoretical Analysis of Curvature Based Preference Models. EMO 2013: 367-382 - [p1]Michael T. M. Emmerich, André H. Deutz, Johannes W. Kruisselbrink:
On Quality Indicators for Black-Box Level Set Approximation. EVOLVE 2013: 157-185 - 2012
- [c11]Michael Emmerich, André H. Deutz:
Time Complexity and Zeros of the Hypervolume Indicator Gradient Field. EVOLVE (III) 2012: 169-193 - 2011
- [c10]Michael T. M. Emmerich, André H. Deutz, Jan Willem Klinkenberg:
Hypervolume-based expected improvement: Monotonicity properties and exact computation. IEEE Congress on Evolutionary Computation 2011: 2147-2154 - [c9]Johannes W. Kruisselbrink, Edgar Reehuis, André H. Deutz, Thomas Bäck, Michael Emmerich:
Using the uncertainty handling CMA-ES for finding robust optima. GECCO 2011: 877-884 - 2010
- [c8]Johannes W. Kruisselbrink, Michael T. M. Emmerich, André H. Deutz, Thomas Bäck:
A robust optimization approach using Kriging metamodels for robustness approximation in the CMA-ES. IEEE Congress on Evolutionary Computation 2010: 1-8 - [c7]Michael Emmerich, André H. Deutz, Rui Li, Johannes W. Kruisselbrink:
Getting lost or getting trapped: on the effect of moves toincomparable points in multiobjective hillclimbing. GECCO (Companion) 2010: 1963-1966 - [c6]Johannes W. Kruisselbrink, Michael Emmerich, André H. Deutz, Thomas Bäck:
Exploiting Overlap When Searching for Robust Optima. PPSN (1) 2010: 63-72 - [c5]Tobias Wagner, Michael Emmerich, André H. Deutz, Wolfgang Ponweiser:
On Expected-Improvement Criteria for Model-based Multi-objective Optimization. PPSN (1) 2010: 718-727
2000 – 2009
- 2008
- [c4]Jan Willem Klinkenberg, Michael T. M. Emmerich, André H. Deutz, Ofer M. Shir, Thomas Bäck:
A Reduced-Cost SMS-EMOA Using Kriging, Self-Adaptation, and Parallelization. MCDM 2008: 301-311 - 2007
- [c3]Michael T. M. Emmerich, André H. Deutz:
Test Problems Based on Lamé Superspheres. EMO 2007: 922-936 - [c2]André H. Deutz, Rudy van Vliet, Hendrik Jan Hoogeboom:
High Spies (or How to Win a Programming Contest). FUN 2007: 93-107 - [c1]Michael T. M. Emmerich, André H. Deutz, Nicola Beume:
Gradient-Based/Evolutionary Relay Hybrid for Computing Pareto Front Approximations Maximizing the S-Metric. Hybrid Metaheuristics 2007: 140-156
1990 – 1999
- 1994
- [j2]André H. Deutz, Andrzej Ehrenfeucht, Grzegorz Rozenberg:
Hyperedge Channels are Abelian. Theor. Comput. Sci. 127(2): 387-393 (1994) - [j1]André H. Deutz, Andrzej Ehrenfeucht, Grzegorz Rozenberg:
Clans and Regions in 2-Structures. Theor. Comput. Sci. 129(2): 207-262 (1994)
Coauthor Index
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