default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDMichael T. M. Emmerich
Michael Emmerich 0001
Person information
- affiliation: University of Jyväskylä, Faculty of Information Technology, Finland
- affiliation: Leiden University, Leiden Institute of Advanced Computer Science (LIACS), The Netherlands
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[j51]Burak Gülmez
, Michael Emmerich, Yingjie Fan:
Multi-objective Optimization for Green Delivery Routing Problems with Flexible Time Windows. Appl. Artif. Intell. 38(1) (2024)- [j50]Pouya Aghaei Pour, Sunith Bandaru, Bekir Afsar, Michael Emmerich, Kaisa Miettinen:
A Performance Indicator for Interactive Evolutionary Multiobjective Optimization Methods. IEEE Trans. Evol. Comput. 28(3): 778-787 (2024) - [c129]Maomao Liang, Babooshka Shavazipour, Bhupinder Singh Saini, Michael Emmerich, Kaisa Miettinen:
A Modified Preference-Based Hypervolume Indicator for Interactive Evolutionary Multiobjective Optimization Methods. IJCCI 2024: 214-221 - [e12]Michael T. M. Emmerich, Vasyl Lytvyn, Victoria Vysotska:
Proceedings of the Modern Data Science Technologies Workshop (MoDaST-2024), Lviv, Ukraine, May 31 - June 1, 2024. CEUR Workshop Proceedings 3723, CEUR-WS.org 2024 [contents] - [e11]Michael Emmerich, Vasyl Lytvyn, Victoria Vysotska:
Proceedings of the Modern Machine Learning Technologies Workshop (MoMLeT 2024), Lviv, Ukraine, May 31 - June 1, 2024. CEUR Workshop Proceedings 3711, CEUR-WS.org 2024 [contents] - [i27]Michael T. M. Emmerich, André H. Deutz:
Multicriteria Optimization and Decision Making: Principles, Algorithms and Case Studies. CoRR abs/2407.00359 (2024) - [i26]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
- [j49]Hui Wang, Michael Emmerich, Mike Preuss, Aske Plaat:
Analysis of Hyper-Parameters for AlphaZero-Like Deep Reinforcement Learning. Int. J. Inf. Technol. Decis. Mak. 22(2): 829-853 (2023) - [j48]Michael Emmerich, André H. Deutz, Iryna Yevseyeva:
Preface. Nat. Comput. 22(2): 225-226 (2023) - [j47]Dani Irawan, Boris Naujoks, Thomas Bäck, Michael Emmerich:
Dominance-based variable analysis for large-scale multi-objective problems. Nat. Comput. 22(2): 243-257 (2023) - [j46]Juhuhn Kim, Michael T. M. Emmerich, Robert H. M. Voors, Barend Ording, Jong-Seok Lee:
A Systematic Approach to Identify Shipping Emissions Using Spatio-Temporally Resolved TROPOMI Data. Remote. Sens. 15(13): 3453 (2023) - [c128]André H. Deutz, Michael Emmerich, Hao Wang:
The Hypervolume Indicator Hessian Matrix: Analytical Expression, Computational Time Complexity, and Sparsity. EMO 2023: 405-418 - [c127]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 - [c126]Divyam Aggarwal, Dhish Kumar Saxena, Thomas Bäck, Michael Emmerich:
Real-World Airline Crew Pairing Optimization: Customized Genetic Algorithm Versus Column Generation Method. EMO 2023: 518-531 - [c125]Ofer M. Shir, Michael Emmerich:
On the Behavior of the Mixed-Integer SMS-EMOA on Box-Constrained Quadratic Bi-Objective Models. GECCO Companion 2023: 1579-1586 - [p6]Dimo Brockhoff, Michael Emmerich, Boris Naujoks, Robin C. Purshouse:
Introduction to Many-Criteria Optimization and Decision Analysis. Many-Criteria Optimization and Decision Analysis 2023: 3-28 - [p5]André H. Deutz, Michael Emmerich, Yali Wang:
Many-Criteria Dominance Relations. Many-Criteria Optimization and Decision Analysis 2023: 81-111 - [p4]Vitor Basto-Fernandes, Diana Salvador, Iryna Yevseyeva, Michael Emmerich:
Many-Criteria Optimisation and Decision Analysis Ontology and Knowledge Management. Many-Criteria Optimization and Decision Analysis 2023: 337-354 - [e10]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] - [e9]Michael Emmerich, Victoria Vysotska, Volodymyr Lytvynenko:
Proceedings of the Modern Machine Learning Technologies and Data Science Workshop (MoMLeT&DS 2023), Lviv, Ukraine, June 3, 2023. CEUR Workshop Proceedings 3426, CEUR-WS.org 2023 [contents] - [e8]Dimo Brockhoff, Michael Emmerich, Boris Naujoks, Robin C. Purshouse:
Many-Criteria Optimization and Decision Analysis: State-of-the-Art, Present Challenges, and Future Perspectives. Natural Computing Series, Springer 2023, ISBN 978-3-031-25262-4 [contents] - [d6]Kyle Eyvindson, Daniel Burgas, Markus Hartikainen, Clara Antón-Fernández, Jussi Hakanen, Michael Emmerich, Johanna Lundström, Mikko Mönkkönen, Tord Snäll, Astor Toraño Caicoya, Marta Vergarechea, Clemens Blattert:
MultiOptForest: An interactive multi-objective optimization tool for forest planning and scenario analysis. Zenodo, 2023
- [d5]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
- [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] - [d4]Clemens Blattert, Mikko Mönkkönen, Daniel Burgas, Fulvio Di Fulvio, Astor Toraño Caicoya, Marta Vergarechea, Julian Klein, Markus Hartikainen, Clara Antón-Fernández, Rasmus Astrup, Michael Emmerich, Nicklas Forsell, Jani Lukkarinen, Johanna Lundström, Samuli Pitzén, Werner Poschenrieder, Eeva Primmer, Tord Snäll, Kyle Eyvindson:
Climate targets in European timber-producing countries conflict with goals on forest ecosystem services and biodiversity. Version 1.0.1. Zenodo, 2022 [all versions] - [d3]Clemens Blattert, Mikko Mönkkönen, Daniel Burgas, Fulvio Di Fulvio, Astor Toraño Caicoya, Marta Vergarechea, Julian Klein, Markus Hartikainen, Clara Antón-Fernández, Rasmus Astrup, Michael Emmerich, Nicklas Forsell, Jani Lukkarinen, Johanna Lundström, Samuli Pitzén, Werner Poschenrieder, Eeva Primmer, Tord Snäll, Kyle Eyvindson:
MultiOptForest Optimization Notebook (V1.0). Zenodo, 2022 - [d2]Clemens Blattert, Mikko Mönkkönen, Daniel Burgas, Fulvio Di Fulvio, Astor Toraño Caicoya, Marta Vergarechea, Julian Klein, Markus Hartikainen, Clara Antón-Fernández, Rasmus Astrup, Michael Emmerich, Nicklas Forsell, Jani Lukkarinen, Johanna Lundström, Samuli Pitzén, Werner Poschenrieder, Eeva Primmer, Tord Snäll, Kyle Eyvindson:
Climate targets in European timber-producing countries conflict with goals on forest ecosystem services and biodiversity. Version 1.0.1. Zenodo, 2022 [all versions] - [d1]Clemens Blattert, Mikko Mönkkönen, Daniel Burgas, Fulvio Di Fulvio, Astor Toraño Caicoya, Marta Vergarechea, Julian Klein, Markus Hartikainen, Clara Antón-Fernández, Rasmus Astrup, Michael Emmerich, Nicklas Forsell, Jani Lukkarinen, Johanna Lundström, Samuli Pitzén, Werner Poschenrieder, Eeva Primmer, Tord Snäll, Kyle Eyvindson:
Climate targets in European timber-producing countries conflict with goals on forest ecosystem services and biodiversity. Version 1.0.1. Zenodo, 2022 [all versions] - [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, Kees 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, Vitor 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, Vitor 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, Vitor Basto-Fernandes, José Ramón 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 Ramón 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, Vitor 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, Vitor 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, Vitor 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, Vitor 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, Vitor 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, Vitor 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]