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ORCIDThomas Bäck
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- affiliation: Leiden Institute of Advanced Computer Science, Netherlands
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2020 – today
- 2020
[j36]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)- [j35]Carola Doerr, Furong Ye, Naama Horesh, Hao Wang, Ofer M. Shir, Thomas Bäck:
Benchmarking discrete optimization heuristics with IOHprofiler. Appl. Soft Comput. 88: 106027 (2020) - [j34]Mozhan Soltani, Felienne Hermans, Thomas Bäck:
The significance of bug report elements. Empir. Softw. Eng. 25(6): 5255-5294 (2020) - [c198]Alexander Hagg, Mike Preuss, Alexander Asteroth, Thomas Bäck:
An Analysis of Phenotypic Diversity in Multi-solution Optimization. BIOMA 2020: 43-55 - [c197]Markus Thill, Wolfgang Konen, Thomas Bäck:
Time Series Encodings with Temporal Convolutional Networks. BIOMA 2020: 161-173 - [c196]Anna V. Kononova, Fabio Caraffini, Hao Wang, Thomas Bäck:
Can Single Solution Optimisation Methods Be Structurally Biased? CEC 2020: 1-9 - [c195]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 - [c194]Diederick Vermetten, Hao Wang, Thomas Bäck, Carola Doerr:
Towards dynamic algorithm selection for numerical black-box optimization: investigating BBOB as a use case. GECCO 2020: 654-662 - [c193]Diederick Vermetten, Hao Wang, Carola Doerr, Thomas Bäck:
Integrated vs. sequential approaches for selecting and tuning CMA-ES variants. GECCO 2020: 903-912 - [c192]Ofer M. Shir, Thomas Bäck:
Sequential experimentation by evolutionary algorithms. GECCO Companion 2020: 957-974 - [c191]Hao Wang, Carola Doerr, Ofer M. Shir, Thomas Bäck:
Benchmarking and analyzing iterative optimization heuristics with IOHprofiler. GECCO Companion 2020: 1043-1054 - [c190]Rick Boks, Hao Wang, Thomas Bäck:
A modular hybridization of particle swarm optimization and differential evolution. GECCO Companion 2020: 1418-1425 - [c189]Alexander Hagg, Alexander Asteroth, Thomas Bäck:
A Deep Dive Into Exploring the Preference Hypervolume. ICCC 2020: 394-397 - [c188]Thiago Rios, Bas van Stein, Stefan Menzel, Thomas Bäck, Bernhard Sendhoff, Patricia Wollstadt:
Feature Visualization for 3D Point Cloud Autoencoders. IJCNN 2020: 1-9 - [c187]Ullah Ullah, Zhao Xu, Hao Wang, Stefan Menzel, Bernhard Sendhoff, Thomas Bäck:
Exploring Clinical Time Series Forecasting with Meta-Features in Variational Recurrent Models. IJCNN 2020: 1-9 - [c186]Jiawen Kong, Thiago Rios, Wojtek Kowalczyk, Stefan Menzel, Thomas Bäck:
On the Performance of Oversampling Techniques for Class Imbalance Problems. PAKDD (2) 2020: 84-96 - [c185]Alexander Hagg, Dominik Wilde, Alexander Asteroth, Thomas Bäck:
Designing Air Flow with Surrogate-Assisted Phenotypic Niching. PPSN (1) 2020: 140-153 - [c184]Anna V. Kononova, Fabio Caraffini, Hao Wang, Thomas Bäck:
Can Compact Optimisation Algorithms Be Structurally Biased? PPSN (1) 2020: 229-242 - [c183]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 - [c182]Jiawen Kong, Wojtek Kowalczyk, Stefan Menzel, Thomas Bäck:
Improving Imbalanced Classification by Anomaly Detection. PPSN (1) 2020: 512-523 - [c181]Furong Ye, Hao Wang, Carola Doerr, Thomas Bäck:
Benchmarking a (μ +λ ) Genetic Algorithm with Configurable Crossover Probability. PPSN (2) 2020: 699-713 - [c180]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 - [c179]Thiago Rios, Jiawen Kong, Bas van Stein, Thomas Bäck, Patricia Wollstadt, Bernhard Sendhoff, Stefan Menzel:
Back To Meshes: Optimal Simulation-ready Mesh Prototypes For Autoencoder-based 3D Car Point Clouds. SSCI 2020: 942-949 - [c178]Raphael Patrick Prager, Heike Trautmann, Hao Wang, Thomas Bäck, Pascal Kerschke:
Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis. SSCI 2020: 996-1003 - [c177]Bas van Stein, Hao Wang, Thomas Bäck:
Neural Network Design: Learning from Neural Architecture Search. SSCI 2020: 1341-1349 - [c176]Yali Wang, Bas van Stein, Thomas Bäck, Michael Emmerich:
A Tailored NSGA-III for Multi-objective Flexible Job Shop Scheduling. SSCI 2020: 2746-2753 - [c175]Sibghat Ullah, Duc Anh Nguyen, Hao Wang, Stefan Menzel, Bernhard Sendhoff, Thomas Bäck:
Exploring Dimensionality Reduction Techniques for Efficient Surrogate-Assisted optimization. SSCI 2020: 2965-2974 - [c174]Milan Koch, Hao Wang, Robert Bürgel, Thomas Bäck:
Towards Data-driven Services in Vehicles. VEHITS 2020: 45-52 - [e7]Juan Julián Merelo Guervós, Jonathan M. Garibaldi, Christian Wagner, Thomas Bäck, Kurosh Madani, Kevin Warwick:
Proceedings of the 12th International Joint Conference on Computational Intelligence, IJCCI 2020, Budapest, Hungary, November 2-4, 2020. SCITEPRESS 2020, ISBN 978-989-758-475-6 [contents] - [e6]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] - [e5]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] - [i33]Andrés Camero, Hao Wang, Enrique Alba, Thomas Bäck:
Bayesian Neural Architecture Search using A Training-Free Performance Metric. CoRR abs/2001.10726 (2020) - [i32]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) - [i31]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) - [i30]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) - [i29]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) - [i28]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) - [i27]Anna V. Kononova, Fabio Caraffini, Thomas Bäck:
Differential evolution outside the box. CoRR abs/2004.10489 (2020) - [i26]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) - [i25]Furong Ye, Hao Wang, Carola Doerr, Thomas Bäck:
Benchmarking a $(μ+λ)$ Genetic Algorithm with Configurable Crossover Probability. CoRR abs/2006.05889 (2020) - [i24]Diederick Vermetten, Hao Wang, Carola Doerr, Thomas Bäck:
Towards Dynamic Algorithm Selection for Numerical Black-Box Optimization: Investigating BBOB as a Use Case. CoRR abs/2006.06586 (2020) - [i23]Hugo Manuel Proença, Peter Grünwald, Thomas Bäck, Matthijs van Leeuwen:
Discovering outstanding subgroup lists for numeric targets using MDL. CoRR abs/2006.09186 (2020) - [i22]Rick Boks, Hao Wang, Thomas Bäck:
A Modular Hybridization of Particle Swarm Optimization and Differential Evolution. CoRR abs/2006.11886 (2020) - [i21]Hao Wang, Diederick Vermetten, Furong Ye, Carola Doerr, Thomas Bäck:
IOHanalyzer: Performance Analysis for Iterative Optimization Heuristic. CoRR abs/2007.03953 (2020) - [i20]Bas van Stein, Hao Wang, Thomas Bäck:
Neural Network Design: Learning from Neural Architecture Search. CoRR abs/2011.00521 (2020) - [i19]Veysel Kocaman, Ofer M. Shir, Thomas Bäck:
Improving Model Accuracy for Imbalanced Image Classification Tasks by Adding a Final Batch Normalization Layer: An Empirical Study. CoRR abs/2011.06319 (2020)
2010 – 2019
- 2019
- [j33]Hao Wang, Michael Emmerich, Thomas Bäck:
Mirrored Orthogonal Sampling for Covariance Matrix Adaptation Evolution Strategies. Evol. Comput. 27(4): 699-725 (2019) - [j32]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) - [j31]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) - [c173]Milan Koch, Victor Geraedts, Hao Wang, Martijn Tannemaat, Thomas Bäck:
Automated Machine Learning for EEG-Based Classification of Parkinson's Disease Patients. BigData 2019: 4845-4852 - [c172]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 - [c171]Furong Ye, Carola Doerr, Thomas Bäck:
Interpolating Local and Global Search by Controlling the Variance of Standard Bit Mutation. CEC 2019: 2292-2299 - [c170]Hao Wang, Yitan Lou, Thomas Bäck:
Hyper-Parameter Optimization for Improving the Performance of Grammatical Evolution. CEC 2019: 2649-2656 - [c169]Yali Wang, Michael Emmerich, André H. Deutz, Thomas Bäck:
Diversity-Indicator Based Multi-Objective Evolutionary Algorithm: DI-MOEA. EMO 2019: 346-358 - [c168]Alexander Hagg, Alexander Asteroth, Thomas Bäck:
Modeling user selection in quality diversity. GECCO 2019: 116-124 - [c167]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 - [c166]Samineh Bagheri, Wolfgang Konen, Thomas Bäck:
Solving optimization problems with high conditioning by means of online whitening. GECCO (Companion) 2019: 243-244 - [c165]Naama Horesh, Thomas Bäck, Ofer M. Shir:
Predict or screen your expensive assay: DoE vs. surrogates in experimental combinatorial optimization. GECCO 2019: 274-284 - [c164]Pierluigi Frisco, Timo Bootsma, Thomas Bäck:
Multi-objective genetic algorithms for reducing mark read-out effort in lithographic tests. GECCO (Companion) 2019: 365-366 - [c163]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 - [c162]Diederick Vermetten, Sander van Rijn, Thomas Bäck, Carola Doerr:
Online selection of CMA-ES variants. GECCO 2019: 951-959 - [c161]Ofer M. Shir, Thomas Bäck:
Sequential experimentation by evolutionary algorithms. GECCO (Companion) 2019: 1095-1112 - [c160]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 - [c159]Borja Calvo, Ofer M. Shir, Josu Ceberio, Carola Doerr, Hao Wang, Thomas Bäck, José Antonio Lozano:
Bayesian performance analysis for black-box optimization benchmarking. GECCO (Companion) 2019: 1789-1797 - [c158]Carola Doerr, Furong Ye, Naama Horesh, Hao Wang, Ofer M. Shir, Thomas Bäck:
Benchmarking discrete optimization heuristics with IOHprofiler. GECCO (Companion) 2019: 1798-1806 - [c157]Sneha Saha, Thiago Rios, Stefan Menzel, Bernhard Sendhoff, Thomas Bäck, Xin Yao, Zhao Xu, Patricia Wollstadt:
Learning Time-Series Data of Industrial Design Optimization using Recurrent Neural Networks. ICDM Workshops 2019: 785-792 - [c156]Bas van Stein, Hao Wang, Thomas Bäck:
Automatic Configuration of Deep Neural Networks with Parallel Efficient Global Optimization. IJCNN 2019: 1-7 - [c155]Sina Däubener, Sebastian Schmitt, Hao Wang, Peter Krause, Thomas Bäck:
Anomaly Detection in Univariate Time Series: An Empirical Comparison of Machine Learning Algorithms. ICDM 2019: 161-175 - [c154]Markus Thill, Sina Däubener, Wolfgang Konen, Thomas Bäck:
Anomaly Detection in Electrocardiogram Readings with Stacked LSTM Networks. ITAT 2019: 17-25 - [c153]Can Wang, Thomas Bäck, Holger H. Hoos, Mitra Baratchi, Steffen Limmer, Markus Olhofer:
Automated Machine Learning for Short-term Electric Load Forecasting. SSCI 2019: 314-321 - [c152]Thiago Rios, Bernhard Sendhoff, Stefan Menzel, Thomas Bäck, Bas van Stein:
On the Efficiency of a Point Cloud Autoencoder as a Geometric Representation for Shape Optimization. SSCI 2019: 791-798 - [c151]Sibghat Ullah, Hao Wang, Stefan Menzel, Bernhard Sendhoff, Thomas Bäck:
An Empirical Comparison of Meta-Modeling Techniques for Robust Design Optimization. SSCI 2019: 819-828 - [c150]Thiago Rios, Patricia Wollstadt, Bas van Stein, Thomas Bäck, Zhao Xu, Bernhard Sendhoff, Stefan Menzel:
Scalability of Learning Tasks on 3D CAE Models Using Point Cloud Autoencoders. SSCI 2019: 1367-1374 - [c149]Xin Guo, Bas van Stein, Thomas Bäck:
A New Approach Towards the Combined Algorithm Selection and Hyper-parameter Optimization Problem. SSCI 2019: 2042-2049 - [c148]Teddy Etoeharnowo, Koen Castelein, Hao Wang, Thomas Bäck:
Switching Between Swarm Optimization Algorithms During a Run: An Empirical Study. SSCI 2019: 2295-2302 - [c147]Jiawen Kong, Wojtek Kowalczyk, Duc Anh Nguyen, Thomas Bäck, Stefan Menzel:
Hyperparameter Optimisation for Improving Classification under Class Imbalance. SSCI 2019: 3072-3078 - [i18]Furong Ye, Carola Doerr, Thomas Bäck:
Interpolating Local and Global Search by Controlling the Variance of Standard Bit Mutation. CoRR abs/1901.05573 (2019) - [i17]Diederick Vermetten, Sander van Rijn, Thomas Bäck, Carola Doerr:
Online Selection of CMA-ES Variants. CoRR abs/1904.07801 (2019) - [i16]Samineh Bagheri, Wolfgang Konen, Thomas Bäck:
SACOBRA with Online Whitening for Solving Optimization Problems with High Conditioning. CoRR abs/1904.08397 (2019) - [i15]Kaifeng Yang, Michael Emmerich, André H. Deutz, Thomas Bäck:
Efficient Computation of Expected Hypervolume Improvement Using Box Decomposition Algorithms. CoRR abs/1904.12672 (2019) - [i14]Alexander Hagg, Alexander Asteroth, Thomas Bäck:
Modeling User Selection in Quality Diversity. CoRR abs/1907.06912 (2019) - [i13]Diederick Vermetten, Hao Wang, Carola Doerr, Thomas Bäck:
Sequential vs. Integrated Algorithm Selection and Configuration: A Case Study for the Modular CMA-ES. CoRR abs/1912.05899 (2019) - [i12]Carola Doerr, Furong Ye, Naama Horesh, Hao Wang, Ofer M. Shir, Thomas Bäck:
Benchmarking Discrete Optimization Heuristics with IOHprofiler. CoRR abs/1912.09237 (2019) - 2018
- [c146]Hao Wang, Michael Emmerich, Thomas Bäck:
Cooling Strategies for the Moment-Generating Function in Bayesian Global Optimization. CEC 2018: 1-8 - [c145]Sheir Yarkoni, Aske Plaat, Thomas Bäck:
First Results Solving Arbitrarily Structured Maximum Independent Set Problems Using Quantum Annealing. CEC 2018: 1-6 - [c144]Thomas Bäck:
Algorithms for Simulation-Based Optimization Problems. ECMS 2018: 5-7 - [c143]Koen van der Blom, Thomas Bäck:
A new foraging-based algorithm for online scheduling. GECCO 2018: 53-60 - [c142]Sander van Rijn, Sebastian Schmitt, Markus Olhofer, Matthijs van Leeuwen, Thomas Bäck:
Multi-fidelity surrogate model approach to optimization. GECCO (Companion) 2018: 225-226 - [c141]Hao Wang, Thomas Bäck:
Ranking empirical cumulative distribution functions using stochastic and pareto dominance. GECCO (Companion) 2018: 257-258 - [c140]Carola Doerr, Furong Ye, Sander van Rijn, Hao Wang, Thomas Bäck:
Towards a theory-guided benchmarking suite for discrete black-box optimization heuristics: profiling (1 + λ) EA variants on onemax and leadingones. GECCO 2018: 951-958 - [c139]Ofer M. Shir, Thomas Bäck:
Sequential experimentation by evolutionary algorithms. GECCO (Companion) 2018: 956-976 - [c138]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 - [c137]Ofer M. Shir, Carola Doerr, Thomas Bäck:
Compiling a benchmarking test-suite for combinatorial black-box optimization: a position paper. GECCO (Companion) 2018: 1753-1760 - [c136]Milan Koch, Hao Wang, Thomas Bäck:
Machine Learning for Predicting the Damaged Parts of a Low Speed Vehicle Crash. ICDIM 2018: 179-184 - [c135]Milan Koch, Thomas Bäck:
Machine Learning for Predicting the Impact Point of a Low Speed Vehicle Crash. ICMLA 2018: 1432-1437 - [c134]Theodoros Georgiou, Sebastian Schmitt, Markus Olhofer, Yu Liu, Thomas Bäck, Michael S. Lew:
Learning Fluid Flows. IJCNN 2018: 1-8 - [c133]Bas van Stein, Hao Wang, Wojtek Kowalczyk, Thomas Bäck:
A Novel Uncertainty Quantification Method for Efficient Global Optimization. IPMU (3) 2018: 480-491 - [c132]Roy de Winter, Bas van Stein, Matthys Dijkman, Thomas Bäck:
Designing Ships Using Constrained Multi-objective Efficient Global Optimization. LOD 2018: 191-203 - [c131]Sander van Rijn, Carola Doerr, Thomas Bäck:
Towards an Adaptive CMA-ES Configurator. PPSN (1) 2018: 54-65 - [c130]Alexander Hagg, Alexander Asteroth, Thomas Bäck:
Prototype Discovery Using Quality-Diversity. PPSN (1) 2018: 500-511 - [c129]Hugo Manuel Proença, Ruben Klijn, Thomas Bäck, Matthijs van Leeuwen:
Identifying flight delay patterns using diverse subgroup discovery. SSCI 2018: 60-67 - [i11]Alexander Hagg, Alexander Asteroth, Thomas Bäck:
Prototype Discovery using Quality-Diversity. CoRR abs/1807.09488 (2018) - [i10]Carola Doerr, Furong Ye, Sander van Rijn, Hao Wang, Thomas Bäck:
Towards a Theory-Guided Benchmarking Suite for Discrete Black-Box Optimization Heuristics: Profiling (1+λ) EA Variants on OneMax and LeadingOnes. CoRR abs/1808.05850 (2018) - [i9]Carola Doerr, Hao Wang, Furong Ye, Sander van Rijn, Thomas Bäck:
IOHprofiler: A Benchmarking and Profiling Tool for Iterative Optimization Heuristics. CoRR abs/1810.05281 (2018) - [i8]Bas van Stein, Hao Wang, Thomas Bäck:
Automatic Configuration of Deep Neural Networks with EGO. CoRR abs/1810.05526 (2018) - 2017
- [j30]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) - [j29]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) - [j28]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) - [c128]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 - [c127]Markus Thill, Wolfgang Konen, Thomas Bäck:
Online anomaly detection on the webscope S5 dataset: A comparative study. EAIS 2017: 1-8 - [c126]Hao Wang, André H. Deutz, Thomas Bäck, Michael Emmerich:
Hypervolume Indicator Gradient Ascent Multi-objective Optimization. EMO 2017: 654-669 - [c125]Sander van Rijn, Hao Wang, Bas van Stein, Thomas Bäck:
Algorithm configuration data mining for CMA evolution strategies. GECCO 2017: 737-744 - [c124]Ofer M. Shir, Thomas Bäck, Joshua D. Knowles, Richard Allmendinger:
Sequential experimentation by evolutionary algorithms. GECCO (Companion) 2017: 828-851 - [c123]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 - [c122]Marco Schönfelder, Valentin Protschky, Thomas Bäck:
Reconstructing fixed time traffic light cycles by camera data analytics. ITSC 2017: 1-7 - [c121]Thierry van der Spek, Bas van Stein, Marcel van der Holst, Thomas Bäck:
A multi-method simulation of a high-frequency bus line. ITSC 2017: 1-6 - [c120]Sheir Yarkoni, Hao Wang, Aske Plaat, Thomas Bäck:
Boosting Quantum Annealing Performance Using Evolution Strategies for Annealing Offsets Tuning. QTOP@NetSys 2017: 157-168 - [c119]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 - [i7]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) - [i6]Martin Hofmann, Florian Neukart, Thomas Bäck:
Artificial Intelligence and Data Science in the Automotive Industry. CoRR abs/1709.01989 (2017) - 2016
- [j27]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) - [j26]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) - [c118]