This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 766186.

Publications

2019 Conference Proceedings

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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

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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

2019 Outreach Activities

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Participated Member: Stephen Friess
Activity Details: Stephen gave a short talk within a session of the popular science segment ‘Knowledge2Go’ about the roots and applications of artificial intelligence and introduced project ECOLE to a wider audience.
Presentation: Slides for Knowledge2Go

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Participated Member: Jiawen (Fay) Kong
Activity Details: Fay presented her research work to other researchers in Doctoral Consortium and got some feedback from the mentors. She also introduced ECOLE program and our research interest to the audience.
Presentation: Slides for Doctoral Consortium

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