Experience-based Computation: Learning to Optimise

Informazioni relative al progetto

ECOLE

ID dell’accordo di sovvenzione: 766186

DOI

10.3030/766186

Progetto chiuso

Data della firma CE 10 Agosto 2017

Data di avvio 1 Aprile 2018

Data di completamento 31 Marzo 2022

Finanziato da

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Costo totale

€ 2 060 348,04

Contributo UE

€ 2 060 348,04

Coordinato da

THE UNIVERSITY OF BIRMINGHAM
United Kingdom

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Demonstrators, pilots, prototypes (2)

Summer schools & Career events

D6.1, D6.2, D6.3, D6.4 D6.5 Project website, Dissemination & outreach plan, Summer schools and Career events

Dissemination and outreach plan

D6.1, D6.2, D6.3, D6.4 D6.5 Project website, Dissemination & outreach plan, Summer schools and Career events

Documents, reports (17)

Engineering data and descriptors

D1.1, D1.2, D1.3 software tools, benchmark test suite of automotive multi-disciplinary/domain real-world scenario

Experience-based high dimensional & big data assisted optimisation

D21 D21 D23 D24 software tools for high dimensional big data assisted optimisation Constrained Multicriteria optimisation Robustness Uncertainty Modelling in optimisation

Multi-domain optimisation software package based on online experience exploitation in dynamic environments

D11 D12 D13 software tools benchmark test suite of automotive multidisciplinarydomain realworld scenario

Constrained & Multicriteria optimisation

D21 D21 D23 D24 software tools for high dimensional big data assisted optimisation Constrained Multicriteria optimisation Robustness Uncertainty Modelling in optimisation

Robustness & Uncertainty Modelling in experience-based optimisation

D21 D21 D23 D24 software tools for high dimensional big data assisted optimisation Constrained Multicriteria optimisation Robustness Uncertainty Modelling in optimisation

Research and personal skill report, Final

D41 D42 D43 ECOLE training programme Research and personal skill reports Career development reports

Deep structured learning and model space learning for engineering and ICT data

D31 D32 D33 D34 D35 software tools on semisupervised learning for class imbalance problems model space learning and representation learning text mining with deep probabilistic models learning for proactive dynamic and robust optimisation with online feature selection

Dissemination and outreach final report

D61 D62 D63 D64 D65 Project website Dissemination outreach plan Summer schools and Career events

Dissemination and outreach mid-term plan & report

D6.1, D6.2, D6.3, D6.4 D6.5 Project website, Dissemination & outreach plan, Summer schools and Career events

Text mining models for product feature optimisation

D31 D32 D33 D34 D35 software tools on semisupervised learning for class imbalance problems model space learning and representation learning text mining with deep probabilistic models learning for proactive dynamic and robust optimisation with online feature selection

Multi-criteria optimisation software environment based on learning for adaptive feature selection and constraint prediction

D11 D12 D13 software tools benchmark test suite of automotive multidisciplinarydomain realworld scenario

Semi-supervised learning for class imbalance problems

D31 D32 D33 D34 D35 software tools on semisupervised learning for class imbalance problems model space learning and representation learning text mining with deep probabilistic models learning for proactive dynamic and robust optimisation with online feature selection

Integrated software environment (‘Self-Tuning optimisation) and manual

D21 D21 D23 D24 software tools for high dimensional big data assisted optimisation Constrained Multicriteria optimisation Robustness Uncertainty Modelling in optimisation

Integrated software environment and manual

D31 D32 D33 D34 D35 software tools on semisupervised learning for class imbalance problems model space learning and representation learning text mining with deep probabilistic models learning for proactive dynamic and robust optimisation with online feature selection

Research and personal skill report, mid-term

D4.1, D4.2, D4.3 ECOLE training programme, Research and personal skill reports, Career development reports

Online learning for proactive dynamic and robust optimization

D31 D32 D33 D34 D35 software tools on semisupervised learning for class imbalance problems model space learning and representation learning text mining with deep probabilistic models learning for proactive dynamic and robust optimisation with online feature selection

Career development report, yearly

Career development report yearly

Other (2)

Awarding of doctoral degrees
ECOLE-training programme

D4.1, D4.2, D4.3 ECOLE training programme, Research and personal skill reports, Career development reports

Pubblicazioni

Conference proceedings (30)

A new acquisition function for robust Bayesian optimization of unconstrained problems

Autori: Sibghat Ullah, Hao Wang, Stefan Menzel, Bernhard Sendhoff, Thomas Bäck
Pubblicato in: 2021 Genetic and Evolutionary Computation Conference, GECCO 2021, 2021, ISBN 9781450383516
Editore: Association for Computing Machinery
DOI: 10.1145/3449726.3463206

Point2FFD: Learning Shape Representations of Simulation-Ready 3D Models for Engineering Design Optimization

Autori: Thiago Rios, Bas Van Stein, Thomas Bäck, Bernhard Sendhoff, Stefan Menzel
Pubblicato in: 2021 International Conference on 3D Vision (3DV), 2021, ISBN 978-1-6654-2689-3
Editore: IEEE
DOI: 10.1109/3dv53792.2021.00110

Exploring Clinical Time Series Forecasting with Meta-Features in Variational Recurrent Models

Autori: Sibghat Ullah, Zhao Xu, Hao Wang, Stefan Menzel, Bernhard Sendhoff, Thomas Back
Pubblicato in: 2020 International Joint Conference on Neural Networks (IJCNN), 2020, Pagina/e 1-9, ISBN 978-1-7281-6926-2
Editore: IEEE
DOI: 10.1109/ijcnn48605.2020.9207254

Artificial Neural Networks as Feature Extractors in Continuous Evolutionary Optimization

Autori: Stephen Friess,  Peter Tiňo; Zhao Xu; Stefan Menzel; Bernhard Sendhoff; Xin Yao
Pubblicato in: 2021 International Joint Conference on Neural Networks (IJCNN), 2021, ISBN 978-1-6654-4597-9
Editore: IEEE
DOI: 10.1109/ijcnn52387.2021.9533915

Exploring Dimensionality Reduction Techniques for Efficient Surrogate-Assisted Optimization

Autori: Sibghat Ullah, Duc Anh Nguyen, Hao Wang, Stefan Menzel, Bernhard Sendhoff and Thomas Bäck
Pubblicato in: IEEE Symposium Series on Computational Intelligence (SSCI), 2020
Editore: IEEE

Quantifying The Generative Capabilities Of Variational Autoencoders For 3D Car Point Clouds

Autori: Sneha Saha, Stefan Menzel, Leandro Minku, Xin Yao, Bernhard Sendhoff and Patricia Wollstadt
Pubblicato in: 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020
Editore: IEEE

Exploiting Generative Models for Performance Predictions of 3D Car Designs

Autori: Sneha Saha, Thiago Rios, Leandro Minku, Bas Vas Stein, Patricia Wollstadt, Xin Yao, Thomas Back, Bernhard Sendhoff, Stefan Menzel
Pubblicato in: 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2022, ISBN 978-1-7281-9049-5
Editore: IEEE
DOI: 10.1109/ssci50451.2021.9660034

Spatio-Temporal Activity Recognition for Evolutionary Search Behavior Prediction

Autori: S. Friess, P. Tiňo, S. Menzel, Z. Xu, B. Sendhoff and X. Yao
Pubblicato in: 2022 International Joint Conference on Neural Networks (IJCNN), 2022
Editore: IEEE

Exploiting 3D Variational Autoencoders For Interactive Vehicle Design

Autori: S. Saha, L. L. Minku, X. Yao, B. Sendhoff , and S. Menzel
Pubblicato in: 2022 International Design Conference, 2022
Editore: Cambridge Press
DOI: 10.1017/pds.2022.177

Feature Visualization for 3D Point Cloud Autoencoders

Autori: Thiago Rios, Bas van Stein, Stefan Menzel, Thomas Back, Bernhard Sendhoff, Patricia Wollstadt
Pubblicato in: 2020 International Joint Conference on Neural Networks (IJCNN), 2020, Pagina/e 1-9, ISBN 978-1-7281-6926-2
Editore: IEEE
DOI: 10.1109/ijcnn48605.2020.9207326

Learning Sparsity of Representations with Discrete Latent Variables

Autori: Zhao Xu,  Daniel Onoro Rubio, Giuseppe Serra, Mathias Niepert
Pubblicato in: 2021 International Joint Conference on Neural Networks (IJCNN), 2021, ISBN 978-1-6654-4597-9
Editore: IEEE
DOI: 10.1109/ijcnn52387.2021.9533762

Interpreting Node Embedding with Text-labeled Graphs

Autori: Giuseppe Serra, Zhao Xu, Mathias Niepert, Carolin Lawrence, Peter Tiňo, Xin Yao
Pubblicato in: 2021 International Joint Conference on Neural Networks (IJCNN), 2021, ISBN 978-1-6654-4597-9
Editore: IEEE
DOI: 10.1109/ijcnn52387.2021.9533692

Learning Time-Series Data of Industrial Design Optimization using Recurrent Neural Networks

Autori: Sneha Saha, Thiago Rios, Stefan Menzel, Bernhard Sendhoff, Thomas Back, Xin Yao, Zhao Xu, Patricia Wollstadt
Pubblicato in: 2019 International Conference on Data Mining Workshops (ICDMW), 2019, Pagina/e 785-792, ISBN 978-1-7281-4896-0
Editore: IEEE
DOI: 10.1109/icdmw.2019.00116

Optimal Evolutionary Optimization Hyper-parameters to Mimic Human User Behavior

Autori: Sneha Saha, Thiago Rios, Leandro L. Minku, Xin Yao, Zhao Xu, Bernhard Sendhoff, Stefan Menzel
Pubblicato in: 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019, Pagina/e 858-866, ISBN 978-1-7281-2485-8
Editore: IEEE
DOI: 10.1109/ssci44817.2019.9002958

Learning Transferable Variation Operators in a Continuous Genetic Algorithm

Autori: Stephen Friess, Peter Tino, Stefan Menzel, Bernhard Sendhoff, Xin Yao
Pubblicato in: 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019, Pagina/e 2027-2033, ISBN 978-1-7281-2485-8
Editore: IEEE
DOI: 10.1109/ssci44817.2019.9002976

When and How to Transfer Knowledge in Dynamic Multi-objective Optimization

Autori: Gan Ruan, Leandro L. Minku, Stefan Menzel, Bernhard Sendhoff, Xin Yao
Pubblicato in: 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019, Pagina/e 2034-2041, ISBN 978-1-7281-2485-8
Editore: IEEE
DOI: 10.1109/ssci44817.2019.9002815

On the Efficiency of a Point Cloud Autoencoder as a Geometric Representation for Shape Optimization

Autori: Thiago Rios, Bernhard Sendhoff, Stefan Menzel, Thomas Back, Bas van Stein
Pubblicato in: 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019, Pagina/e 791-798, ISBN 978-1-7281-2485-8
Editore: IEEE
DOI: 10.1109/ssci44817.2019.9003161

Hyperparameter Optimisation for Improving Classification under Class Imbalance

Autori: Jiawen Kong, Wojtek Kowalczyk, Duc Anh Nguyen, Thomas Back, Stefan Menzel
Pubblicato in: 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019, Pagina/e 3072-3078, ISBN 978-1-7281-2485-8
Editore: IEEE
DOI: 10.1109/ssci44817.2019.9002679

Scalability of Learning Tasks on 3D CAE Models Using Point Cloud Autoencoders

Autori: Thiago Rios, Patricia Wollstadt, Bas van Stein, Thomas Back, Zhao Xu, Bernhard Sendhoff, Stefan Menzel
Pubblicato in: 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019, Pagina/e 1367-1374, ISBN 978-1-7281-2485-8
Editore: IEEE
DOI: 10.1109/ssci44817.2019.9002982

An Empirical Comparison of Meta-Modeling Techniques for Robust Design Optimization

Autori: Sibghat Ullah, Hao Wang, Stefan Menzel, Bernhard Sendhoff, Thomas Back
Pubblicato in: 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019, Pagina/e 819-828, ISBN 978-1-7281-2485-8
Editore: IEEE
DOI: 10.1109/ssci44817.2019.9002805

Predicting CMA-ES Operators as Inductive Biases for Shape Optimization Problems

Autori: Stephen Friess, Peter Tiňo, Stefan Menzel, Bernhard Sendhoff, Xin Yao
Pubblicato in: 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021, ISBN 978-1-7281-9049-5
Editore: IEEE
DOI: 10.1109/ssci50451.2021.9660001

Exploiting Linear Interpolation of Variational Autoencoders for Satisfying Preferences in Evolutionary Design Optimization

Autori: Sneha Saha; Leandro L. Minku; Xin Yao; Bernhard Senhoff; Stefan Menzel
Pubblicato in: 2021 IEEE Congress on Evolutionary Computation (CEC), 2021, ISBN 978-1-7281-8394-7
Editore: IEEE
DOI: 10.1109/cec45853.2021.9504772

Representing Experience in Continuous Evolutionary optimisation through Problem-tailored Search Operators

Autori: Stephen Friess, Peter Tino, Stefan Menzel, Bernhard Sendhoff, Xin Yao
Pubblicato in: 2020 IEEE Congress on Evolutionary Computation (CEC), 2020, Pagina/e 1-7, ISBN 978-1-7281-6929-3
Editore: IEEE
DOI: 10.1109/cec48606.2020.9185687

Efficient AutoML via Combinational Sampling

Autori: Duc Anh Nguyen, Anna V. Kononova, Stefan Menzel, Bernhard Sendhoff, Thomas Back
Pubblicato in: 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021, ISBN 978-1-7281-9049-5
Editore: IEEE
DOI: 10.1109/ssci50451.2021.9660073

Split-AE: An Autoencoder-based Disentanglement Framework for 3D Shape-to-shape Feature Transfer

Autori: Sneha Saha, Leandro Minku, Xin Yao, Bernard Sendhoff, Stefan Menzel
Pubblicato in: 2022 International Joint Conference on Neural Networks (IJCNN), 2022
Editore: IEEE

Back to Meshes: Optimal Simulation-ready mesh prototypes for autoencoder based 3D car point clouds

Autori: Thiago Rios, Jiawen Kong, Bas van Stein, Thomas Bäck, Patricia Wollstadt, Bernhard Sendhoff and Stefan Menzel
Pubblicato in: 2020 IEEE Symposium Series on Computational Intelligence SSCI, 2020
Editore: IEEE

Improved Sample Type Identification for Multi-Class Imbalanced Classification with Real-World Applications

Autori: J. Kong, W. Kowalczyk, K. Jonker, S. Menzel and T. Bäck
Pubblicato in: the 18th Int. Conference on Data Science (ICDATA’22), 2022
Editore: Springer

A Systematic Approach to Analyze the Computational Cost of Robustness in Model-Assisted Robust Optimization

Autori: S. Ullah, H. Wang, S. Menzel, B. Sendhoff and T. Bäck
Pubblicato in: 2022 International Conference on Parallel Problem Solving from Nature, 2022
Editore: Springer

Improved Automated CASH Optimization with Tree Parzen Estimators for Class Imbalance Problems

Autori: Duc Anh Nguyen, Jiawen Kong, Hao Wang, Stefan Menzel, Bernhard Sendhoff, Anna V. Kononova, Thomas Bäck
Pubblicato in: 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), 2021, ISBN 978-1-6654-2100-3
Editore: IEEE
DOI: 10.1109/dsaa53316.2021.9564147

Computational Study on Effectiveness of Knowledge Transfer in Dynamic Multi-objective Optimization

Autori: Gan Ruan, Leandro L. Minku, Stefan Menzel, Bernhard Sendhoff, Xin Yao
Pubblicato in: 2020 IEEE Congress on Evolutionary Computation (CEC), 2020, Pagina/e 1-8, ISBN 978-1-7281-6929-3
Editore: IEEE
DOI: 10.1109/cec48606.2020.9185907

Book chapters (3)

Improving Sampling in Evolution Strategies Through Mixture-Based Distributions Built from Past Problem Instances

Autori: Stephen Friess, Peter Tiňo, Stefan Menzel, Bernhard Sendhoff, Xin Yao
Pubblicato in: Parallel Problem Solving from Nature – PPSN XVI - 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part I, Numero 12269, 2020, Pagina/e 583-596, ISBN 978-3-030-58111-4
Editore: Springer International Publishing
DOI: 10.1007/978-3-030-58112-1_40

Improving Imbalanced Classification by Anomaly Detection

Autori: Jiawen Kong, Wojtek Kowalczyk, Stefan Menzel, Thomas Bäck
Pubblicato in: Parallel Problem Solving from Nature – PPSN XVI - 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part I, Numero 12269, 2020, Pagina/e 512-523, ISBN 978-3-030-58111-4
Editore: Springer International Publishing
DOI: 10.1007/978-3-030-58112-1_35

On the Performance of Oversampling Techniques for Class Imbalance Problems

Autori: Jiawen Kong, Thiago Rios, Wojtek Kowalczyk, Stefan Menzel, Thomas Bäck
Pubblicato in: Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Singapore, May 11–14, 2020, Proceedings, Part II, Numero 12085, 2020, Pagina/e 84-96, ISBN 978-3-030-47435-5
Editore: Springer International Publishing
DOI: 10.1007/978-3-030-47436-2_7

Other (1)

Exploiting Local Geometric Features in Vehicle Design Optimization with 3D Point Cloud Autoencoders

Autori: Thiago Rios, Bas van Stein, Patricia Wollstadt, Thomas Bäck, Bernhard Sendhoff, Stefan Menzel
Pubblicato in: 2021 IEEE Congress on Evolutionary Computation (CEC), 2021
Editore: IEEE
DOI: 10.1109/cec45853.2021.9504746

Peer reviewed articles (2)

Multi-Task Shape Optimization Using a 3D Point Cloud Autoencoder as Unified Representation

Autori: Thiago Rios, Bas van Stein, Thomas Bäck, Bernhard Sendhoff, Stefan Menzel
Pubblicato in: IEEE Transactions on Evolutionary Computation , 2021, ISSN 1089-778X
Editore: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tevc.2021.3086308

An Efficient Contesting Procedure for AutoML Optimization

Autori: D.A. Nguyen, A.V. Kononova, S. Menzel, B. Sendhoff and T. Bäck
Pubblicato in: IEEE Access, 2022, ISSN 2169-3536
Editore: Institute of Electrical and Electronics Engineers Inc.
DOI: 10.1109/access.2022.3192036