This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 766186. 
Ullah, H. Wang, S. Menzel, B. Sendhoff and T. Bäck, “A Systematic Approach to Analyze the Computational Cost of Robustness in Model-Assisted Robust Optimization,” In the International Conference on Parallel Problem Solving from Nature, Springer, 2022.
A Systematic Approach to Analyze the Computational Cost of Robustness in Model-Assisted Robust Optimization
Kong, W. Kowalczyk, K. Jonker, S. Menzel and T. Bäck, ” Improved Sample Type Identification for Multi-Class Imbalanced Classification with Real-World Applications,” in the 18th Int. Conference on Data Science (ICDATA’22), 2022.
Improved Sample Type Identification for Multi-Class Imbalanced Classification with Real-World Applications
S. Friess, P. Tiňo, S. Menzel, Z. Xu, B. Sendhoff and X. Yao, “Spatio-Temporal Activity Recognition for Evolutionary Search Behavior Prediction,” In IEEE International Joint Conference on Neural Networks (IJCNN), 2022.
Spatio-Temporal Activity Recognition for Evolutionary Search Behavior Prediction
A. Nguyen, A. V. Kononova, S. Menzel, B. Sendhoff and T. Bäck, “An Efficient Contesting Procedure for AutoML Optimization,” in IEEE Access, vol. 10, pp. 75754-75771, 2022, doi: 10.1109/ACCESS.2022.3192036.
An Efficient Contesting Procedure for AutoML Optimization
T. Rios, B. van Stein, P. Wollstadt, T. Bäck, B. Sendhoff and S. Menzel, “Exploiting
Local Geometric Features in Vehicle Design Optimization with 3D Point Cloud
Autoencoders,” 2021 IEEE Congress on Evolutionary Computation (CEC), 2021, pp.
514-521, doi: 10.1109/CEC45853.2021.9504746.
Exploiting Local Geometric Features in Vehicle Design Optimization with 3D Point Cloud Autoencoders
S. Saha, L. L. Minku, X. Yao, Z. Xu, B. Sendhoff, and S. Menzel, “Exploiting Linear
Interpolation of Variational Autoencoders for Satisfying Preferences in
Evolutionary Design Optimization”, in IEEE Congress on Evolutionary Computation
(CEC) 2021.
Exploiting Linear Interpolation of Variational Autoencoders for Satisfying Preferences in Evolutionary Design Optimization
T. Rios, B. van Stein, T. Bäck, B. Sendhoff and S. Menzel, “Multi-Task Shape Optimization Using a 3D Point Cloud Autoencoder as Unified Representation,” in IEEE Transactions on Evolutionary Computation, doi: 10.1109/TEVC.2021.3086308
Multitask Shape Optimization Using a 3-D Point Cloud Autoencoder as Unified Representation
T. Rios, B. Van Stein, T. Bäck, B. Sendhoff and S. Menzel, “Point2FFD: Learning
Shape Representations of Simulation-Ready 3D Models for Engineering Design
Optimization,” 2021 International Conference on 3D Vision (3DV), 2021, pp. 1024-
1033, doi: 10.1109/3DV53792.2021.00110
Point2FFD: Learning Shape Representations of Simulation-Ready 3D Models for Engineering Design Optimization
S. Saha, T. Rios, L. L. Minku, B. Stein. P. Wollstadt, X. Yao, B. Sendhoff, and S.
Menzel, “Exploiting Generative Models for Performance Predictions of 3D Car
Designs”, in Symposium Series on Computational Intelligence (SSCI) 2021.
Exploiting Generative Models for Performance Predictions of 3D Car Designs
D.A. Nguyen, A.V. Kononova, S. Menzel, B. Sendhoff and T. Bäck, “Efficient
AutoML via combinational sampling,” IEEE Symposium Series on Computational
Intelligence (IEEE SSCI), 2021
Efficient AutoML via Combinational Sampling
S. Friess, P. Tiňo, S. Menzel, B. Sendhoff and X. Yao, “Predicting CMA-ES
Operators as Inductive Biases for Shape Optimization Problems,” In 2021 In IEEE
Symposium Series on Computational Intelligence (SSCI)
Predicting CMA-ES Operators as Inductive Biases for Shape Optimization Problems
S. Ullah, H. Wang, S. Menzel, B. Sendhoff and T. Bäck, “A New Acquisition
Function for Robust Bayesian Optimization of Unconstrained Problems,” In
Genetic and Evolutionary Computation Conference Companion (GECCO ’21
Companion), July 10–14, Lille, France, 2021.
A new acquisition function for robust Bayesian optimization of unconstrained problems
D.A. Nguyen, J. Kong, H. Wang, S. Menzel, B. Sendhoff, A.V. Kononova and T.
Bäck, “Improved Automated CASH Optimization with Tree Parzen Estimators for
Class Imbalance Problems,” in the 8th IEEE International Conference on Data
Science and Advanced Analytics (DSAA), 2021.
Improved Automated CASH Optimization with Tree Parzen Estimators for Class Imbalance Problems
S. Friess, P. Tiňo, Z. Xu, S. Menzel, B. Sendhoff and X. Yao, “Artificial Neural
Networks as Feature Extractors in Continuous Evolutionary Optimization,” In IEEE
International Joint Conference on Neural Networks (IJCNN), 2021.
Artificial Neural Networks as Feature Extractors in Continuous Evolutionary Optimization
G. Serra, Z. Xu, M. Niepert, C. Lawrence, P. Tino and X.Yao, “Interpreting Node
Embedding with Text-labeled Graphs,” 2021 International Joint Conference on
Neural Networks (IJCNN), 2021.
Interpreting Node Embedding with Text-labeled Graphs
Z. Xu, D. Onoro-Rubio, G. Serra, M. Niepert, “Learning Sparsity of
Representations with Discrete Latent Variables,” 2021 International Joint
Conference on Neural Networks (IJCNN), 2021.
Learning Sparsity of Representations with Discrete Latent Variables
Rodrigo G. F. Soares and Leandro L. Minku. “OSNN: An Online Semisupervised
Neural Network for Nonstationary Data Streams”, IEEE Transactions on Neural
Networks and Learning Systems, 2021
OSNN: An Online Semisupervised Neural Network for Nonstationary Data Streams
Thiago Rios, Jiawen Kong, Bas van Stein, Thomas Bäck, Patricia Wollstadt, Bernhard Sendhoff and Stefan Menzel, “Back To Meshes: Optimal Simulation-ready Mesh Prototypes For Autoencoder-based 3D Car Point Clouds”, in 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia, 1-4 December 2020.
Back To Meshes: Optimal Simulation-ready Mesh Prototypes For Autoencoder-based 3D Car Point Clouds
Sneha Saha, Stefan Menzel, Leandro Minku, Xin Yao, Bernhard Sendhoff and Patricia Wollstadt, “Quantifying The Generative Capabilities Of Variational Autoencoders For 3D Car Point Clouds”, in 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia, 1-4 December 2020.
Quantifying The Generative Capabilities Of Variational Autoencoders For 3D Car Point Clouds
Sibghat Ullah, Duc Anh Nguyen, Hao Wang, Stefan Menzel, Bernhard Sendhoff and Thomas Bäck, “Exploring Dimensionality Reduction Techniques for Efficient Surrogate-Assisted Optimization”, in 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia, 1-4 December 2020.
Exploring Dimensionality Reduction Techniques for Efficient Surrogate-Assisted Optimization
Jiawen Kong, Wojtek Kowalczyk, Stefan Menzel and Thomas Bäck, “Improving Imbalanced Classification by Anomaly Detection” in Sixteenth International Conference on Parallel Problem Solving from Nature (PPSN), Leiden, The Netherlands, 5-9 September 2020.
Improving Imbalanced Classification by Anomaly Detection
Stephen Friess, Peter Tiňo, Stefan Menzel, Bernhard Sendhoff and Xin Yao, “Improving Sampling in Evolution Strategies through Mixture-based Distributions built from Past Problem Instances” in Sixteenth International Conference on Parallel Problem Solving from Nature (PPSN), Leiden, The Netherlands, 5-9 September 2020.
Improving Sampling in Evolution Strategies through Mixture-based Distributions built from Past Problem Instances
Thiago Rios, Bas van Stein, Stefan Menzel, Thomas Bäck, Bernhard Sendhoff and Patricia Wollstadt, “Feature Visualization for 3D Point Cloud Autoencoders” in 2020 IEEE International Joint Conference on Neural Networks (IJCNN), Glasgow, United Kingdom, 19-24 July 2020.
Feature Visualization for 3D Point Cloud Autoencoders
Sibghat Ullah, Zhao Xu, Hao Wang, Stefan Menzel, Bernhard Sendhoff, and Thomas Bäck, “Exploring Clinical Time Series Forecasting with Meta-Features in Variational Recurrent Models” in 2020 IEEE International Joint Conference on Neural Networks (IJCNN), Glasgow, United Kingdom, 19-24 July 2020.
Exploring Clinical Time Series Forecasting with Meta-Features in Variational Recurrent Models
Stephen Friess, Peter Tiňo, Stefan Menzel, Bernhard Sendhoff and Xin Yao, “Representing Experience in Continuous Evolutionary Optimisation through Problem-tailored Search Operators” in 2020 IEEE Congress on Evolutionary Computation (IEEE CEC), Glasgow, United Kingdom, 19-24 July 2020.
Representing Experience in Continuous Evolutionary Optimisation through Problem-tailored Search Operators
Gan Ruan, Leandro L. Minku, Stefan Menzel, Bernhard Sendhoff and Xin Yao, “Computational Study on Effectiveness of Knowledge Transfer in Dynamic Multi-objective Optimization” in 2020 IEEE Congress on Evolutionary Computation (IEEE CEC), Glasgow, United Kingdom, 19-24 July 2020.
Computational Study on Effectiveness of Knowledge Transfer in Dynamic Multi-objective Optimization
Jiawen Kong, Thiago Rios, Wojtek Kowalczyk, Stefan Menzel and Thomas Bäck, “On the Performance of Oversampling Techniques for Class Imbalance Problems” in the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Singapore, 11-14 May 2020.
On the Performance of Oversampling Techniques for Class Imbalance Problems
Thiago Rios, Thomas Bäck, Bas van Stein, Bernhard Sendhoff and Stefan Menzel, “On the Efficiency of a Point Cloud Autoencoder as a Geometric Representation for Shape Optimization” in 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 6-9 December 2019.
On the Efficiency of a Point Cloud Autoencoder as a Geometric Representation for Shape Optimization
Thiago Rios, Patricia Wollstadt, Bas van Stein, Thomas Bäck, Zhao Xu, Bernhard Sendhoff and Stefan Menzel, “Scalability of Learning Tasks on 3D CAE Models using Point Cloud Autoencoders”, in 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 6-9 December 2019.
Scalability of Learning Tasks on 3D CAE Models using Point Cloud Autoencoders
Sneha Saha, Thiago Rios, Leandro Minku, Xin Yao, Zhao Xu, Bernhard Sendhoff and Stefan Menzel, “Optimal Evolutionary Optimization Hyperparameters to Mimic Human User Behaviour”, in 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 6-9 December 2019.
Optimal Evolutionary Optimization Hyperparameters to Mimic Human User Behaviour
Sibghat Ullah, Hao Wang, Stefan Menzel, Bernhard Sendhoff and Thomas Bäck, “An Empirical Comparison of Meta-Modeling Techniques for Robust Design Optimization”, in 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 6-9 December 2019.
An Empirical Comparison of Meta-Modeling Techniques for Robust Design Optimization
Jiawen Kong, Wojtek Kowalczyk, Duc Anh Nguyen, Stefan Menzel and Thomas Bäck, “Hyperparameter Optimisation for Improving Classification under Class Imbalance”, in 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 6-9 December 2019.
Hyperparameter Optimisation for Improving Classification under Class Imbalance
Stephen Friess, Peter Tiňo, Stefan Menzel, Bernhard Sendhoff and Xin Yao, “Learning Transferable Variation Operators in a Continuous Genetic Algorithm”, in 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 6-9 December 2019.
Learning Transferable Variation Operators in a Continuous Genetic Algorithm
Gan Ruan, Leandro Minku, Stefan Menzel, Bernhard Sendhoff and Xin Yao, “When and How to Transfer Knowledge in Dynamic Multi-objective Optimization”, in 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 6-9 December 2019.
When and How to Transfer Knowledge in Dynamic Multi-objective Optimization
Sneha Saha, Thiago Rios, Stefan Menzel, Bernhard Sendhoff, Thomas Bäck, Xin Yao, Zhao Xu and Patricia Wollstadt, “Learning Time-series Data of Industrial Design Optimization using Recurrent Neural Networks”, in LMID workshop of IEEE International Conference on Data Mining (ICDM), Beijing, China, 8-11 November 2019.
Learning Time-series Data of Industrial Design Optimization using Recurrent Neural Networks