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ORCIDThomas Bäck
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- affiliation: Leiden Institute of Advanced Computer Science, Netherlands
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2020 – today
- 2021
[j40]Andrés Camero, Hao Wang, Enrique Alba, Thomas Bäck
:
Bayesian neural architecture search using a training-free performance metric. Appl. Soft Comput. 106: 107356 (2021)- [j39]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) - [j38]Jianyong Sun, Xin Liu, Thomas Bäck, Zongben Xu:
Learning Adaptive Differential Evolution Algorithm From Optimization Experiences by Policy Gradient. IEEE Trans. Evol. Comput. 25(4): 666-680 (2021) - [c217]Alexander Zeiser, Bas van Stein, Thomas Bäck:
Requirements towards optimizing analytics in industrial processes. ANT/EDI40 2021: 597-605 - [c216]Thiago Rios, Bas van Stein, Patricia Wollstadt, Thomas Bäck, Bernhard Sendhoff, Stefan Menzel:
Exploiting Local Geometric Features in Vehicle Design Optimization with 3D Point Cloud Autoencoders. CEC 2021: 514-521 - [c215]Roy de Winter, Bas van Stein, Thomas Bäck:
SAMO-COBRA: A Fast Surrogate Assisted Constrained Multi-objective Optimization Algorithm. EMO 2021: 270-282 - [c214]Charles Moussa, Hao Wang, Henri Calandra, Thomas Bäck, Vedran Dunjko:
Tabu-Driven Quantum Neighborhood Samplers. EvoCOP 2021: 100-119 - [c213]Furong Ye, Carola Doerr, Thomas Bäck:
Leveraging benchmarking data for informed one-shot dynamic algorithm selection. GECCO Companion 2021: 245-246 - [c212]Jacob de Nobel, Hao Wang, Thomas Bäck:
Explorative data analysis of time series based algorithm features of CMA-ES variants. GECCO 2021: 510-518 - [c211]Alexander Hagg, Sebastian Berns, Alexander Asteroth, Simon Colton, Thomas Bäck:
Expressivity of parameterized and data-driven representations in quality diversity search. GECCO 2021: 678-686 - [c210]Ofer M. Shir, Thomas Bäck:
Sequential experimentation by evolutionary algorithms. GECCO Companion 2021: 941-958 - [c209]Diederick Vermetten, Anna V. Kononova, Fabio Caraffini, Hao Wang, Thomas Bäck:
Is there anisotropy in structural bias? GECCO Companion 2021: 1243-1250 - [c208]Sibghat Ullah, Hao Wang, Stefan Menzel, Bernhard Sendhoff, Thomas Bäck:
A new acquisition function for robust Bayesian optimization of unconstrained problems. GECCO Companion 2021: 1344-1345 - [c207]Jacob de Nobel, Diederick Vermetten, Hao Wang, Carola Doerr, Thomas Bäck:
Tuning as a means of assessing the benefits of new ideas in interplay with existing algorithmic modules. GECCO Companion 2021: 1375-1384 - [c206]Anna V. Kononova, Ofer M. Shir, Teus Tukker, Pierluigi Frisco, Shutong Zeng, Thomas Bäck:
Addressing the multiplicity of solutions in optical lens design as a niching evolutionary algorithms computational challenge. GECCO Companion 2021: 1596-1604 - [c205]Sheir Yarkoni, Andreas Huck, Hanno Schülldorf, Benjamin Speitkamp, Marc Shakory Tabrizi, Martin Leib, Thomas Bäck, Florian Neukart:
Solving the Shipment Rerouting Problem with Quantum Optimization Techniques. ICCL 2021: 502-517 - [c204]Marios Kefalas, Mitra Baratchi, Asteris Apostolidis, Dirk van den Herik, Thomas Bäck:
Automated Machine Learning for Remaining Useful Life Estimation of Aircraft Engines. ICPHM 2021: 1-9 - [i53]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) - [i52]Jianyong Sun, Xin Liu, Thomas Bäck, Zongben Xu:
Learning adaptive differential evolution algorithm from optimization experiences by policy gradient. CoRR abs/2102.03572 (2021) - [i51]Furong Ye, Carola Doerr, Thomas Bäck:
Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm Selection. CoRR abs/2102.06481 (2021) - [i50]Jacob de Nobel, Diederick Vermetten, Hao Wang, Carola Doerr, Thomas Bäck:
Tuning as a Means of Assessing the Benefits of New Ideas in Interplay with Existing Algorithmic Modules. CoRR abs/2102.12905 (2021) - [i49]Sander van Rijn, Sebastian Schmitt, Matthijs van Leeuwen, Thomas Bäck:
Finding Efficient Trade-offs in Multi-Fidelity Response Surface Modeling. CoRR abs/2103.03280 (2021) - [i48]Theodoros Georgiou, Sebastian Schmitt, Thomas Bäck, Nan Pu, Wei Chen, Michael S. Lew:
PREPRINT: Comparison of deep learning and hand crafted features for mining simulation data. CoRR abs/2103.06552 (2021) - [i47]Theodoros Georgiou, Sebastian Schmitt, Thomas Bäck, Wei Chen, Michael S. Lew:
Preprint: Norm Loss: An efficient yet effective regularization method for deep neural networks. CoRR abs/2103.06583 (2021) - [i46]Hugo Manuel Proença, Thomas Bäck, Matthijs van Leeuwen:
Robust subgroup discovery. CoRR abs/2103.13686 (2021) - [i45]David Von Dollen, Florian Neukart, Daniel Weimer, Thomas Bäck:
Quantum-Assisted Feature Selection for Vehicle Price Prediction Modeling. CoRR abs/2104.04049 (2021) - [i44]Jacob de Nobel, Hao Wang, Thomas Bäck:
Explorative Data Analysis of Time Series based AlgorithmFeatures of CMA-ES Variants. CoRR abs/2104.08098 (2021) - [i43]Alexander Hagg, Sebastian Berns, Alexander Asteroth, Simon Colton, Thomas Bäck:
Expressivity of Parameterized and Data-driven Representations in Quality Diversity Search. CoRR abs/2105.04247 (2021) - [i42]Alexander Hagg, Mike Preuss, Alexander Asteroth, Thomas Bäck:
An Analysis of Phenotypic Diversity in Multi-Solution Optimization. CoRR abs/2105.04252 (2021) - [i41]Alexander Hagg, Dominik Wilde, Alexander Asteroth, Thomas Bäck:
Designing Air Flow with Surrogate-assisted Phenotypic Niching. CoRR abs/2105.04256 (2021) - [i40]Diederick Vermetten, Anna V. Kononova, Fabio Caraffini, Hao Wang, Thomas Bäck:
Is there Anisotropy in Structural Bias? CoRR abs/2105.04480 (2021) - [i39]Anna V. Kononova, Ofer M. Shir, Teus Tukker, Pierluigi Frisco, Shutong Zeng, Thomas Bäck:
Addressing the Multiplicity of Solutions in Optical Lens Design as a Niching Evolutionary Algorithms Computational Challenge. CoRR abs/2105.10541 (2021) - [i38]Furong Ye, Carola Doerr, Hao Wang, Thomas Bäck:
Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance. CoRR abs/2106.06304 (2021) - [i37]Annie Wong, Thomas Bäck, Anna V. Kononova, Aske Plaat:
Multiagent Deep Reinforcement Learning: Challenges and Directions Towards Human-Like Approaches. CoRR abs/2106.15691 (2021) - [i36]Danny Weyns, Thomas Bäck, René Vidal, Xin Yao, Ahmed Nabil Belbachir:
Lifelong Computing. CoRR abs/2108.08802 (2021) - [i35]Sheir Yarkoni, Alex Alekseyenko, Michael Streif, David Von Dollen, Florian Neukart, Thomas Bäck:
Multi-car paint shop optimization with quantum annealing. CoRR abs/2109.07876 (2021) - [i34]Veysel Kocaman, Ofer M. Shir, Thomas Bäck:
The Unreasonable Effectiveness of the Final Batch Normalization Layer. CoRR abs/2109.09016 (2021) - 2020
- [j37]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) - [j36]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) - [j35]Mozhan Soltani, Felienne Hermans, Thomas Bäck:
The significance of bug report elements. Empir. Softw. Eng. 25(6): 5255-5294 (2020) - [c203]Marios Kefalas, Milan Koch, Victor Geraedts, Hao Wang, Martijn Tannemaat, Thomas Bäck:
Automated Machine Learning for the Classification of Normal and Abnormal Electromyography Data. IEEE BigData 2020: 1176-1185 - [c202]Alexander Hagg, Mike Preuss, Alexander Asteroth, Thomas Bäck:
An Analysis of Phenotypic Diversity in Multi-solution Optimization. BIOMA 2020: 43-55 - [c201]Markus Thill, Wolfgang Konen, Thomas Bäck:
Time Series Encodings with Temporal Convolutional Networks. BIOMA 2020: 161-173 - [c200]Anna V. Kononova, Fabio Caraffini, Hao Wang, Thomas Bäck:
Can Single Solution Optimisation Methods Be Structurally Biased? CEC 2020: 1-9 - [c199]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 - [c198]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 - [c197]Diederick Vermetten, Hao Wang, Carola Doerr, Thomas Bäck:
Integrated vs. sequential approaches for selecting and tuning CMA-ES variants. GECCO 2020: 903-912 - [c196]Ofer M. Shir, Thomas Bäck:
Sequential experimentation by evolutionary algorithms. GECCO Companion 2020: 957-974 - [c195]Hao Wang, Carola Doerr, Ofer M. Shir, Thomas Bäck:
Benchmarking and analyzing iterative optimization heuristics with IOHprofiler. GECCO Companion 2020: 1043-1054 - [c194]Rick Boks, Hao Wang, Thomas Bäck:
A modular hybridization of particle swarm optimization and differential evolution. GECCO Companion 2020: 1418-1425 - [c193]Alexander Hagg, Alexander Asteroth, Thomas Bäck:
A Deep Dive Into Exploring the Preference Hypervolume. ICCC 2020: 394-397 - [c192]Theodoros Georgiou, Sebastian Schmitt, Thomas Bäck, Nan Pu, Wei Chen, Michael S. Lew:
Comparison of deep learning and hand crafted features for mining simulation data. ICPR 2020: 3396-3403 - [c191]Theodoros Georgiou, Sebastian Schmitt, Thomas Bäck, Wei Chen, Michael S. Lew:
Norm Loss: An efficient yet effective regularization method for deep neural networks. ICPR 2020: 8812-8818 - [c190]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. ICPR 2020: 10404-10411 - [c189]Thiago Rios, Bas van Stein, Stefan Menzel, Thomas Bäck, Bernhard Sendhoff, Patricia Wollstadt:
Feature Visualization for 3D Point Cloud Autoencoders. IJCNN 2020: 1-9 - [c188]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 - [c187]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 - [c186]Hugo Manuel Proença, Peter Grünwald, Thomas Bäck, Matthijs van Leeuwen:
Discovering Outstanding Subgroup Lists for Numeric Targets Using MDL. ECML/PKDD (1) 2020: 19-35 - [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
- [j34]Hao Wang, Michael Emmerich, Thomas Bäck:
Mirrored Orthogonal Sampling for Covariance Matrix Adaptation Evolution Strategies. Evol. Comput. 27(4): 699-725 (2019) - [j33]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) - [j32]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. IEEE 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)