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ORCIDMichael T. M. Emmerich
Michael Emmerich
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- affiliation: Leiden Institute of Advanced Computer Science, Netherlands
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2020 – today
- 2022
[j45]Jesús Guillermo Falcón-Cardona
, Michael T. M. Emmerich, Carlos A. Coello Coello:
On the Construction of Pareto-Compliant Combined Indicators. Evol. Comput. 30(3): 381-408 (2022)- [j44]Bhupinder Singh Saini, Michael Emmerich, Atanu Mazumdar, Bekir Afsar, Babooshka Shavazipour, Kaisa Miettinen:
Optimistic NAUTILUS navigator for multiobjective optimization with costly function evaluations. J. Glob. Optim. 83(4): 865-889 (2022) - [c124]Michael Emmerich, Yulian Kuryliak, Dmytro Dosyn:
Simulation of the Effects of Targeted Immunization on the Peak Number of Infections in Complex Networks. MoMLeT+DS 2022: 1-13 - [e7]Michael Emmerich, Victoria Vysotska:
Modern Machine Learning Technologies and Data Science Workshop MoMLeT&DS 2022, Leiden-Lviv, The Netherlands-Ukraine, November 25-26, 2022. CEUR Workshop Proceedings 3312, CEUR-WS.org 2022 [contents] - [i25]Yulian Kuryliak, Michael Emmerich, Dmytro Dosyn:
Efficient Stochastic Simulation of Network Topology Effects on the Peak Number of Infections in Epidemic Outbreaks. CoRR abs/2202.13325 (2022) - [i24]Hao Wang, Kaifeng Yang, Michael Affenzeller, Michael Emmerich:
Probability Distribution of Hypervolume Improvement in Bi-objective Bayesian Optimization. CoRR abs/2205.05505 (2022) - [i23]Patrick Echtenbruck, Martina Echtenbruck, Joost Batenburg, Thomas Bäck, Boris Naujoks, Michael Emmerich:
Optimally Weighted Ensembles of Regression Models: Exact Weight Optimization and Applications. CoRR abs/2206.11263 (2022) - [i22]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
- [j43]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) - [j42]Xuhan Liu, Kai Ye, Herman W. T. van Vlijmen, Michael T. M. Emmerich, Adriaan P. IJzerman, Gerard J. P. van Westen:
DrugEx v2: de novo design of drug molecules by Pareto-based multi-objective reinforcement learning in polypharmacology. J. Cheminformatics 13(1): 85 (2021) - [j41]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) - [j40]Yali Wang, Steffen Limmer, Markus Olhofer, Michael Emmerich, Thomas Bäck:
Automatic preference based multi-objective evolutionary algorithm on vehicle fleet maintenance scheduling optimization. Swarm Evol. Comput. 65: 100933 (2021) - [j39]Jesús Guillermo Falcón-Cardona, Hisao Ishibuchi, Carlos A. Coello Coello, Michael Emmerich:
On the Effect of the Cooperation of Indicator-Based Multiobjective Evolutionary Algorithms. IEEE Trans. Evol. Comput. 25(4): 681-695 (2021) - [c123]Patrick Echtenbruck, Michael Emmerich, Martina Echtenbruck, Boris Naujoks:
Optimally Weighted Ensembles in Model-Based Regression for Drug Discovery. CEC 2021: 2251-2258 - [c122]Yulian Kuryliak, Michael Emmerich, Dmytro Dosyn:
On the Effect of Complex Network Topology in Managing Epidemic Outbreaks. MoMLeT+DS 2021: 1-15 - [e6]Michael Emmerich, Vasyl Lytvyn, Victoria Vysotska, Vítor Basto Fernandes, Volodymyr Lytvynenko:
Modern Machine Learning Technologies and Data Science Workshop. Proc. 3rd International Workshop (MoMLeT&DS 2021). Volume I: Main Conference, Lviv-Shatsk, Ukraine, June 5-6, 2021. CEUR Workshop Proceedings 2917, CEUR-WS.org 2021 [contents] - [i21]Yali Wang, Steffen Limmer, Markus Olhofer, Michael Emmerich, Thomas Bäck:
Automatic Preference Based Multi-objective Evolutionary Algorithm on Vehicle Fleet Maintenance Scheduling Optimization. CoRR abs/2101.09556 (2021) - 2020
- [j38]Bas van Stein, Hao Wang, Wojtek Kowalczyk, Michael Emmerich, Thomas Bäck:
Cluster-based Kriging approximation algorithms for complexity reduction. Appl. Intell. 50(3): 778-791 (2020) - [j37]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) - [c121]Dani Irawan, Boris Naujoks, Michael Emmerich:
Cooperative-Coevolution-CMA-ES with Two-Stage Grouping. CEC 2020: 1-8 - [c120]Yali Wang, Bas van Stein, Thomas Bäck, Michael Emmerich:
Improving NSGA-III for flexible job shop scheduling using automatic configuration, smart initialization and local search. GECCO Companion 2020: 181-182 - [c119]Oleh Soprun, Myroslava Bublyk, Yurii Matseliukh, Vasyl Andrunyk, Lyubomyr Chyrun, Ivan Dyyak, Anatoly Yakovlev, Michael Emmerich, Oleksandr Osolinskyi, Anatoliy Sachenko:
Forecasting Temperatures of a Synchronous Motor with Permanent Magnets Using Machine Learning. MoMLeT+DS 2020: 95-120 - [c118]Alina Dmytriv, Victoria Vysotska, Petro Kravets, Ihor Karpov, Michael Emmerich:
Trees' Condition Data Analysis Based on Drone Monitoring and Machine Learning Technology. MoMLeT+DS 2020: 433-456 - [c117]Lucas de Almeida Ribeiro, Michael Emmerich, Anderson da Silva Soares, Telma Woerle de Lima:
On Sharing Information Between Sub-populations in MOEA/S. PPSN (2) 2020: 171-185 - [c116]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 - [c115]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 - [c114]Yali Wang, Bas van Stein, Thomas Bäck, Michael Emmerich:
A Tailored NSGA-III for Multi-objective Flexible Job Shop Scheduling. SSCI 2020: 2746-2753 - [c113]Hui Wang, Mike Preuss, Michael Emmerich, Aske Plaat:
Tackling Morpion Solitaire with AlphaZero-like Ranked Reward Reinforcement Learning. SYNASC 2020: 149-152 - [p3]Michael T. M. Emmerich, Kaifeng Yang, André H. Deutz:
Infill Criteria for Multiobjective Bayesian Optimization. High-Performance Simulation-Based Optimization 2020: 3-16 - [e5]Michael Emmerich, Vasyl Lytvyn, Victoria Vysotska, Vítor Basto Fernandes, Volodymyr Lytvynenko:
Proceedings of the 2nd International Workshop on Modern Machine Learning Technologies and Data Science (MoMLeT+DS 2020). Volume I: Main Conference, Lviv-Shatsk, Ukraine, June 2-3, 2020. CEUR Workshop Proceedings 2631, CEUR-WS.org 2020 [contents] - [e4]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] - [e3]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] - [i20]Divyam Aggarwal, Dhish Kumar Saxena, Thomas Bäck, Michael Emmerich:
Real-World Airline Crew Pairing Optimization: Customized Genetic Algorithm versus Column Generation Method. CoRR abs/2003.03792 (2020) - [i19]Divyam Aggarwal, Dhish Kumar Saxena, Thomas Bäck, Michael Emmerich:
AirCROP: Airline Crew Pairing Optimizer for Complex Flight Networks Involving Multiple Crew Bases & Billion-Plus Variables. CoRR abs/2003.03994 (2020) - [i18]Hui Wang, Michael Emmerich, Mike Preuss, Aske Plaat:
Analysis of Hyper-Parameters for Small Games: Iterations or Epochs in Self-Play? CoRR abs/2003.05988 (2020) - [i17]Divyam Aggarwal, Dhish Kumar Saxena, Thomas Bäck, Michael Emmerich:
On Initializing Airline Crew Pairing Optimization for Large-scale Complex Flight Networks. CoRR abs/2003.06423 (2020) - [i16]Yali Wang, Bas van Stein, Michael T. M. Emmerich, Thomas Bäck:
A Tailored NSGA-III Instantiation for Flexible Job Shop Scheduling. CoRR abs/2004.06564 (2020) - [i15]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) - [i14]Divyam Aggarwal, Dhish Kumar Saxena, Thomas Bäck, Michael Emmerich:
A Novel Column Generation Heuristic for Airline Crew Pairing Optimization with Large-scale Complex Flight Networks. CoRR abs/2005.08636 (2020) - [i13]Hui Wang, Mike Preuss, Michael Emmerich, Aske Plaat:
Tackling Morpion Solitaire with AlphaZero-likeRanked Reward Reinforcement Learning. CoRR abs/2006.07970 (2020) - [i12]Michael Emmerich, Joost Nibbeling, Marios Kefalas, Aske Plaat:
Multiple Node Immunisation for Preventing Epidemics on Networks by Exact Multiobjective Optimisation of Cost and Shield-Value. CoRR abs/2010.06488 (2020)
2010 – 2019
- 2019
- [j36]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) - [j35]Hao Wang, Michael Emmerich, Thomas Bäck:
Mirrored Orthogonal Sampling for Covariance Matrix Adaptation Evolution Strategies. Evol. Comput. 27(4): 699-725 (2019) - [j34]David Ruano-Ordás, Iryna Yevseyeva, Vítor Basto Fernandes, José Ramon Méndez, Michael T. M. Emmerich:
Improving the drug discovery process by using multiple classifier systems. Expert Syst. Appl. 121: 292-303 (2019) - [j33]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) - [j32]David Ruano-Ordás, Lindsey Burggraaff, Rongfang Liu, Cas van der Horst, Laura H. Heitman, Michael T. M. Emmerich, José Ramon Méndez, Iryna Yevseyeva, Gerard J. P. van Westen:
A multiple classifier system identifies novel cannabinoid CB2 receptor ligands. J. Cheminformatics 11(1): 66:1-66:14 (2019) - [j31]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) - [j30]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) - [c112]Yali Wang, Steffen Limmer, Markus Olhofer, Michael T. M. Emmerich, Thomas Bäck:
Vehicle Fleet Maintenance Scheduling Optimization by Multi-objective Evolutionary Algorithms. CEC 2019: 442-449 - [c111]Jesús Guillermo Falcón-Cardona, Michael T. M. Emmerich, Carlos A. Coello Coello:
On the Cooperation of Multiple Indicator-based Multi-Objective Evolutionary Algorithms. CEC 2019: 2050-2057 - [c110]Patrick Echtenbruck, Michael Emmerich, Boris Naujoks:
A Multiobjective Approach to Classification in Drug Discovery. CIBCB 2019: 1-8 - [c109]Victoria Vysotska, Vasyl Lytvyn, Yevhen Burov, Pavlo Berezin, Michael Emmerich, Vítor Basto Fernandes:
Development of Information System for Textual Content Categorizing Based on Ontology. COLINS 2019: 53-70 - [c108]Jesús Guillermo Falcón-Cardona, Carlos A. Coello Coello, Michael Emmerich:
CRI-EMOA: A Pareto-Front Shape Invariant Evolutionary Multi-objective Algorithm. EMO 2019: 307-318 - [c107]Yali Wang, Michael Emmerich, André H. Deutz, Thomas Bäck:
Diversity-Indicator Based Multi-Objective Evolutionary Algorithm: DI-MOEA. EMO 2019: 346-358 - [c106]André H. Deutz, Michael Emmerich, Kaifeng Yang:
The Expected R2-Indicator Improvement for Multi-objective Bayesian Optimization. EMO 2019: 359-370 - [c105]Koen van der Blom, Sjonnie Boonstra, Hèrm Hofmeyer, Michael Emmerich:
Analysing Optimisation Data for Multicriteria Building Spatial Design. EMO 2019: 671-682 - [c104]Hao Wang, Thomas Bäck, Aske Plaat, Michael Emmerich, Mike Preuss:
On the potential of evolution strategies for neural network weight optimization. GECCO (Companion) 2019: 191-192 - [c103]Kaifeng Yang, Pramudita Satria Palar, Michael Emmerich, Koji Shimoyama, Thomas Bäck:
A multi-point mechanism of expected hypervolume improvement for parallel multi-objective bayesian global optimization. GECCO 2019: 656-663 - [c102]Assaf Israeli, Michael Emmerich, Michael (Iggy) Litaor, Ofer M. Shir:
Statistical learning in soil sampling design aided by pareto optimization. GECCO 2019: 1198-1205 - [c101]Marios Kefalas, Steffen Limmer, Asteris Apostolidis, Markus Olhofer, Michael Emmerich, Thomas Bäck:
A tabu search-based memetic algorithm for the multi-objective flexible job shop scheduling problem. GECCO (Companion) 2019: 1254-1262 - [c100]Jesús Guillermo Falcón-Cardona, Michael T. M. Emmerich, Carlos A. Coello Coello:
On the construction of pareto-compliant quality indicators. GECCO (Companion) 2019: 2024-2027 - [c99]Hui Wang, Michael Emmerich, Mike Preuss, Aske Plaat:
Alternative Loss Functions in AlphaZero-like Self-play. SSCI 2019: 155-162 - [e2]Michael Emmerich, Vasyl Lytvyn, Iryna Yevseyeva, Vítor Basto Fernandes, Dmytro Dosyn, Victoria Vysotska:
Modern Machine Learning Technologies, Workshop Proceedings of the 8th International Conference on "Mathematics. Information Technologies. Education", MoMLeT&DS-2019, Shatsk, Ukraine, June 2-4, 2019. CEUR Workshop Proceedings 2386, CEUR-WS.org 2019 [contents] - [i11]Hui Wang, Michael Emmerich, Mike Preuss, Aske Plaat:
Hyper-Parameter Sweep on AlphaZero General. CoRR abs/1903.08129 (2019) - [i10]Kaifeng Yang, Michael Emmerich, André H. Deutz, Thomas Bäck:
Efficient Computation of Expected Hypervolume Improvement Using Box Decomposition Algorithms. CoRR abs/1904.12672 (2019) - 2018
- [j29]Longmei Li, Hao Chen, Jun Li, Ning Jing, Michael Emmerich:
Preference-Based Evolutionary Many-Objective Optimization for Agile Satellite Mission Planning. IEEE Access 6: 40963-40978 (2018) - [j28]Sjonnie Boonstra, Koen van der Blom, Hèrm Hofmeyer, Michael T. M. Emmerich, Jos van Schijndel, Pieter de Wilde:
Toolbox for super-structured and super-structure free multi-disciplinary building spatial design optimisation. Adv. Eng. Informatics 36: 86-100 (2018) - [j27]Jiaqi Zhao, Licheng Jiao, Fang Liu, Vítor Basto Fernandes, Iryna Yevseyeva, Shixiong Xia, Michael T. M. Emmerich:
3D fast convex-hull-based evolutionary multiobjective optimization algorithm. Appl. Soft Comput. 67: 322-336 (2018) - [j26]Jiaqi Zhao, Licheng Jiao, Shixiong Xia, Vítor Basto Fernandes, Iryna Yevseyeva, Yong Zhou, Michael T. M. Emmerich:
Multiobjective sparse ensemble learning by means of evolutionary algorithms. Decis. Support Syst. 111: 86-100 (2018) - [j25]Michael T. M. Emmerich, André H. Deutz:
A tutorial on multiobjective optimization: fundamentals and evolutionary methods. Nat. Comput. 17(3): 585-609 (2018) - [j24]Longmei Li, Yali Wang, Heike Trautmann, Ning Jing, Michael Emmerich:
Multiobjective evolutionary algorithms based on target region preferences. Swarm Evol. Comput. 40: 196-215 (2018) - [c98]Hui Wang, Michael Emmerich, Aske Plaat:
Assessing the Potential of Classical Q-learning in General Game Playing. BNCAI 2018: 138-150 - [c97]Hao Wang, Michael Emmerich, Thomas Bäck:
Cooling Strategies for the Moment-Generating Function in Bayesian Global Optimization. CEC 2018: 1-8 - [c96]Victoria Vysotska, Vítor Basto Fernandes, Michael Emmerich:
Web Content Support Method in Electronic Business Systems. COLINS 2018: 20-41 - [c95]Vasyl Lytvyn, Dmytro Dosyn, Michael Emmerich, Iryna Yevseyeva:
Content Formation Method in the Web Systems. COLINS 2018: 42-61 - [c94]Yassine Baghoussi, João Mendes-Moreira, Michael T. M. Emmerich:
Updating a robust optimization model for improving bus schedules. COMSNETS 2018: 619-624 - [c93]Victoria Vysotska, Vítor Basto Fernandes, Vasyl Lytvyn, Michael Emmerich, Mariya Hrendus:
Method for Determining Linguometric Coefficient Dynamics of Ukrainian Text Content Authorship. CSIT 2018: 132-151 - [c92]Bohdan Rusyn, Vasyl Lytvyn, Victoria Vysotska, Michael Emmerich, Liubomyr Pohreliuk:
The Virtual Library System Design and Development. CSIT 2018: 328-349 - [c91]Longmei Li, Hao Chen, Jing Wu, Jun Li, Ning Jing, Michael Emmerich:
Preference-based evolutionary algorithms for many-objective mission planning of agile earth observation satellites. GECCO (Companion) 2018: 187-188 - [c90]Pramudita Satria Palar, Kaifeng Yang, Koji Shimoyama, Michael Emmerich, Thomas Bäck:
Multi-objective aerodynamic design with user preference using truncated expected hypervolume improvement. GECCO 2018: 1333-1340 - [c89]Longmei Li, Hao Chen, Jun Li, Ning Jing, Michael Emmerich:
Integrating region preferences in multiobjective evolutionary algorithms based on decomposition. ICACI 2018: 379-384 - [c88]Gisele Lobo Pappa, Michael T. M. Emmerich, Ana L. C. Bazzan, Will N. Browne, Kalyanmoy Deb, Carola Doerr, Marko Durasevic, Michael G. Epitropakis, Saemundur O. Haraldsson, Domagoj Jakobovic, Pascal Kerschke, Krzysztof Krawiec, Per Kristian Lehre, Xiaodong Li, Andrei Lissovoi, Pekka Malo, Luis Martí, Yi Mei, Juan Julián Merelo Guervós, Julian F. Miller, Alberto Moraglio, Antonio J. Nebro, Su Nguyen, Gabriela Ochoa, Pietro S. Oliveto, Stjepan Picek, Nelishia Pillay, Mike Preuss, Marc Schoenauer, Roman Senkerik, Ankur Sinha, Ofer M. Shir, Dirk Sudholt, L. Darrell Whitley, Mark Wineberg, John R. Woodward, Mengjie Zhang:
Tutorials at PPSN 2018. PPSN (2) 2018: 477-489 - [c87]Divyam Aggarwal, Dhish Kumar Saxena, Michael Emmerich, Saaju Paulose:
On Large-Scale Airline Crew Pairing Generation. SSCI 2018: 593-600 - [e1]Alexandru-Adrian Tantar, Emilia Tantar, Michael Emmerich, Pierrick Legrand, Lenuta Alboaie, Henri Luchian:
EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation VI, EVOLVE 2015, Iasi, Romania, 18-24 June 2015. Advances in Intelligent Systems and Computing 674, Springer 2018, ISBN 978-3-319-69708-6 [contents] - [r1]Michael Emmerich, Ofer M. Shir, Hao Wang:
Evolution Strategies. Handbook of Heuristics 2018: 89-119 - [i9]Hui Wang, Michael Emmerich, Aske Plaat:
Monte Carlo Q-learning for General Game Playing. CoRR abs/1802.05944 (2018) - [i8]Karl Bringmann, Sergio Cabello, Michael T. M. Emmerich:
Maximum Volume Subset Selection for Anchored Boxes. CoRR abs/1803.00849 (2018) - [i7]Hui Wang, Michael Emmerich, Aske Plaat:
Assessing the Potential of Classical Q-learning in General Game Playing. CoRR abs/1810.06078 (2018) - 2017
- [j23]Vítor Basto Fernandes, Iryna Yevseyeva, José Ramon Méndez, Jiaqi Zhao, Florentino Fdez-Riverola, Michael T. M. Emmerich:
Corrigendum to "A spam filtering multi-objective optimization study covering parsimony maximization and three-way classification" [Applied Soft Computing Volume 48 (2016) 111-123]. Appl. Soft Comput. 55: 565 (2017) - [j22]Samineh Bagheri, Wolfgang Konen, Michael Emmerich, Thomas Bäck:
Self-adjusting parameter control for surrogate-assisted constrained optimization under limited budgets. Appl. Soft Comput. 61: 377-393 (2017) - [j21]Jiaqi Zhao, Vítor Basto Fernandes, Licheng Jiao, Iryna Yevseyeva, Asep Maulana, Rui Li, Thomas Bäck, Ke Tang, Michael T. M. Emmerich:
Corrigendum to 'Multiobjective optimization of classifiers by means of 3D convex-hull-based evolutionary algorithms' [Information Sciences volumes 367-368 (2016) 80-104]. Inf. Sci. 403: 55 (2017) - [j20]Zhiwei Yang, Jan-Paul van Osta, Barry D. Van Veen, Rick van Krevelen, Richard van Klaveren, Andries Stam, Joost N. Kok, Thomas Bäck, Michael Emmerich:
Dynamic vehicle routing with time windows in theory and practice. Nat. Comput. 16(1): 119-134 (2017) - [c86]Longmei Li, Feng Yao, Ning Jing, Michael Emmerich:
Preference incorporation to solve multi-objective mission planning of agile earth observation satellites. CEC 2017: 1366-1373 - [c85]Yali Wang, Longmei Li, Kaifeng Yang, Michael T. M. Emmerich:
A new approach to target region based multiobjective evolutionary algorithms. CEC 2017: 1757-1764 - [c84]Koen van der Blom, Sjonnie Boonstra, Hèrm Hofmeyer, Thomas Bäck, Michael T. M. Emmerich:
Configuring advanced evolutionary algorithms for multicriteria building spatial design optimisation. CEC 2017: 1803-1810 - [c83]Asep Maulana, Michael T. M. Emmerich:
Towards many-objective optimization of eigenvector centrality in multiplex networks. CoDIT 2017: 729-734 - [c82]Karl Bringmann, Sergio Cabello, Michael T. M. Emmerich:
Maximum Volume Subset Selection for Anchored Boxes. SoCG 2017: 22:1-22:15 - [c81]Longmei Li, Iryna Yevseyeva, Vítor Basto Fernandes, Heike Trautmann, Ning Jing, Michael Emmerich:
Building and Using an Ontology of Preference-Based Multiobjective Evolutionary Algorithms. EMO 2017: 406-421 - [c80]Hao Wang, André H. Deutz, Thomas Bäck, Michael Emmerich:
Hypervolume Indicator Gradient Ascent Multi-objective Optimization. EMO 2017: 654-669 - [c79]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 - [c78]Hao Wang, Bas van Stein, Michael T. M. Emmerich, Thomas Bäck:
Time complexity reduction in efficient global optimization using cluster kriging. GECCO 2017: 889-896 - [c77]Lai-Yee Liu, Vítor Basto Fernandes, Iryna Yevseyeva, Joost N. Kok, Michael Emmerich:
Indicator-Based Evolutionary Level Set Approximation: Mixed Mutation Strategy and Extended Analysis. IWINAC (1) 2017: 146-159 - [c76]Hao Wang, Bas van Stein, Michael Emmerich, Thomas Bäck:
A new acquisition function for Bayesian optimization based on the moment-generating function. SMC 2017: 507-512 - [c75]Asep Maulana, Marios Kefalas, Michael T. M. Emmerich:
Immunization of networks using genetic algorithms and multiobjective metaheuristics. SSCI 2017: 1-8 - [i6]Bas van Stein, Hao Wang, Wojtek Kowalczyk, Michael T. M. Emmerich, Thomas Bäck:
Cluster-based Kriging Approximation Algorithms for Complexity Reduction. CoRR abs/1702.01313 (2017) - 2016
- [j19]Vítor Basto Fernandes, Iryna Yevseyeva, José Ramon Méndez, Jiaqi Zhao, Florentino Fdez-Riverola, Michael T. M. Emmerich:
A spam filtering multi-objective optimization study covering parsimony maximization and three-way classification. Appl. Soft Comput. 48: 111-123 (2016) - [j18]Zhiwei Yang, Michael Emmerich, Thomas Bäck, Joost N. Kok:
Multi-objective inventory routing with uncertain demand using population-based metaheuristics. Integr. Comput. Aided Eng. 23(3): 205-220 (2016) - [j17]Jiaqi Zhao, Vítor Basto Fernandes, Licheng Jiao, Iryna Yevseyeva, Asep Maulana, Rui Li, Thomas Bäck, Ke Tang, Michael T. M. Emmerich:
Multiobjective optimization of classifiers by means of 3D convex-hull-based evolutionary algorithms. Inf. Sci. 367-368: 80-104 (2016) - [c74]Hao Wang, Michael T. M. Emmerich, Thomas Bäck:
Balancing risk and expected gain in kriging-based global optimization. CEC 2016: 719-727 - [c73]