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Published:
LLaMEA, a pioneering framework combining Evolution and Large Language Models.
Published:
A Journey Through Predictive Maintenance
Published:
An introduction to Structural Bias in search heuristics.
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Gecco 2023
Published:
Gecco 2023
Cross-Industry Predictive Maintenance Optimization Platform
Optimization of Complex Lens Designs
eXplainable AI for Predictive Maintenance
AI for Oversight ICAI lab
Published in EVOLVE-A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV: International Conference held at Leiden University, July 10-13, 2013, 2013
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Recommended citation: Bas Van, Michael Emmerich, Zhiwei Yang, "Fitness landscape analysis of nk landscapes and vehicle routing problems by expanded barrier trees." EVOLVE-A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV: International Conference held at Leiden University, July 10-13, 2013, 2013.
Published in Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA 2015, Saint Etienne. France, October 22-24, 2015. Proceedings, 2015
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Recommended citation: Bas Stein, Hao Wang, Wojtek Kowalczyk, Thomas B{\"a}ck, Michael Emmerich, "Optimally weighted cluster kriging for big data regression." Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA 2015, Saint Etienne. France, October 22-24, 2015. Proceedings, 2015.
Published in 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016
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Recommended citation: Pepijn Van, Bas Stein, Thomas B{\"a}ck, "A framework for evaluating meta-models for simulation-based optimisation." 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016.
Published in Information Processing and Management of Uncertainty in Knowledge-Based Systems: 16th International Conference, IPMU 2016, Eindhoven, The Netherlands, June 20-24, 2016, Proceedings, Part II 16, 2016
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Recommended citation: Bas Van, Wojtek Kowalczyk, "An incremental algorithm for repairing training sets with missing values." Information Processing and Management of Uncertainty in Knowledge-Based Systems: 16th International Conference, IPMU 2016, Eindhoven, The Netherlands, June 20-24, 2016, Proceedings, Part II 16, 2016.
Published in Information Processing and Management of Uncertainty in Knowledge-Based Systems: 16th International Conference, IPMU 2016, Eindhoven, The Netherlands, June 20-24, 2016, Proceedings, Part II 16, 2016
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Recommended citation: Bas Stein, Wojtek Kowalczyk, Thomas B{\"a}ck, "Analysis and visualization of missing value patterns." Information Processing and Management of Uncertainty in Knowledge-Based Systems: 16th International Conference, IPMU 2016, Eindhoven, The Netherlands, June 20-24, 2016, Proceedings, Part II 16, 2016.
Published in 2016 IEEE international conference on fuzzy systems (FUZZ-IEEE), 2016
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Recommended citation: Bas Stein, Hao Wang, Wojtek Kowalczyk, Michael Emmerich, Thomas B{\"a}ck, "Fuzzy clustering for optimally weighted cluster kriging." 2016 IEEE international conference on fuzzy systems (FUZZ-IEEE), 2016.
Published in 2016 IEEE International Conference on Big Data (Big Data), 2016
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Recommended citation: Bas Van, Matthijs Van, Thomas B{\"a}ck, "Local subspace-based outlier detection using global neighbourhoods." 2016 IEEE International Conference on Big Data (Big Data), 2016.
Published in 2016 International Conference on Computational Science and Computational Intelligence (CSCI), 2016
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Recommended citation: Bas Van, Matthijs Van, Hao Wang, Stephan Purr, Sebastian Kreissl, Josef Meinhardt, Thomas B{\"a}ck, "Towards data driven process control in manufacturing car body parts." 2016 International Conference on Computational Science and Computational Intelligence (CSCI), 2016.
Published in 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017
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Recommended citation: Thierry Spek, Bas Stein, Marcel Holst, Thomas B{\"a}ck, "A multi-method simulation of a high-frequency bus line." 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017.
Published in 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017
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Recommended citation: Hao Wang, Bas Stein, Michael Emmerich, Thomas Back, "A new acquisition function for Bayesian optimization based on the moment-generating function." 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017.
Published in Proceedings of the Genetic and Evolutionary Computation Conference, 2017
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Recommended citation: Sander Rijn, Hao Wang, Bas Stein, Thomas B{\"a}ck, "Algorithm configuration data mining for cma evolution strategies." Proceedings of the Genetic and Evolutionary Computation Conference, 2017.
Published in Proceedings of the Genetic and Evolutionary Computation Conference, 2017
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Recommended citation: Hao Wang, Bas Stein, Michael Emmerich, Thomas B{\"a}ck, "Time complexity reduction in efficient global optimization using cluster kriging." Proceedings of the Genetic and Evolutionary Computation Conference, 2017.
Published in Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications: 17th International Conference, IPMU 2018, C'adiz, Spain, June 11-15, 2018, Proceedings, Part III 17, 2018
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Recommended citation: Bas Stein, Hao Wang, Wojtek Kowalczyk, Thomas B{\"a}ck, "A novel uncertainty quantification method for efficient global optimization." Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications: 17th International Conference, IPMU 2018, C'adiz, Spain, June 11-15, 2018, Proceedings, Part III 17, 2018.
Published in 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019
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Recommended citation: Xin Guo, Bas Stein, Thomas B{\"a}ck, "A new approach towards the combined algorithm selection and hyper-parameter optimization problem." 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019.
Published in 2019 International Joint Conference on Neural Networks (IJCNN), 2019
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Recommended citation: Bas Stein, Hao Wang, Thomas B{\"a}ck, "Automatic configuration of deep neural networks with parallel efficient global optimization." 2019 International Joint Conference on Neural Networks (IJCNN), 2019.
Published in Machine Learning, Optimization, and Data Science: 4th International Conference, LOD 2018, Volterra, Italy, September 13-16, 2018, Revised Selected Papers 4, 2019
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Recommended citation: Roy Winter, Bas Stein, Matthys Dijkman, Thomas B{\"a}ck, "Designing ships using constrained multi-objective efficient global optimization." Machine Learning, Optimization, and Data Science: 4th International Conference, LOD 2018, Volterra, Italy, September 13-16, 2018, Revised Selected Papers 4, 2019.
Published in 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019
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Recommended citation: Thiago Rios, Bernhard Sendhoff, Stefan Menzel, Thomas B{\"a}ck, Bas Stein, "On the efficiency of a point cloud autoencoder as a geometric representation for shape optimization." 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019.
Published in 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019
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Recommended citation: Thiago Rios, Patricia Wollstadt, Bas Stein, Thomas B{\"a}ck, Zhao Xu, Bernhard Sendhoff, Stefan Menzel, "Scalability of learning tasks on 3D CAE models using point cloud autoencoders." 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019.
Published in 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020
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Recommended citation: Yali Wang, Bas Stein, Thomas B{\"a}ck, Michael Emmerich, "A tailored NSGA-III for multi-objective flexible job shop scheduling." 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020.
Published in 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020
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Recommended citation: Thiago Rios, Jiawen Kong, Bas Stein, Thomas B{\"a}ck, Patricia Wollstadt, Bernhard Sendhoff, Stefan Menzel, "Back to meshes: Optimal simulation-ready mesh prototypes for autoencoder-based 3D car point clouds." 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020.
Published in Applied Intelligence, 2020
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Recommended citation: Bas Van, Hao Wang, Wojtek Kowalczyk, Michael Emmerich, Thomas B{\"a}ck, "Cluster-based Kriging approximation algorithms for complexity reduction." Applied Intelligence, 2020.
Published in 2020 International Joint Conference on Neural Networks (IJCNN), 2020
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Recommended citation: Thiago Rios, Bas Stein, Stefan Menzel, Thomas Back, Bernhard Sendhoff, Patricia Wollstadt, "Feature visualization for 3D point cloud autoencoders." 2020 International Joint Conference on Neural Networks (IJCNN), 2020.
Published in Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, 2020
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Recommended citation: Yali Wang, Bas Stein, Thomas B{\"a}ck, Michael Emmerich, "Improving NSGA-III for flexible job shop scheduling using automatic configuration, smart initialization and local search." Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, 2020.
Published in 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020
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Recommended citation: Bas Stein, Hao Wang, Thomas B{\"a}ck, "Neural network design: learning from neural architecture search." 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020.
Published in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2021
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Recommended citation: Bas Stein, Fabio Caraffini, Anna Kononova, "Emergence of structural bias in differential evolution." Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2021.
Published in 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021
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Recommended citation: Sneha Saha, Thiago Rios, Leandro Minku, Bas Stein, Patricia Wollstadt, Xin Yao, Thomas Back, Bernhard Sendhoff, Stefan Menzel, "Exploiting generative models for performance predictions of 3D car designs." 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021.
Published in 2021 IEEE Congress on Evolutionary Computation (CEC), 2021
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Recommended citation: Thiago Rios, Bas Stein, Patricia Wollstadt, Thomas B{\"a}ck, Bernhard Sendhoff, Stefan Menzel, "Exploiting local geometric features in vehicle design optimization with 3D point cloud autoencoders." 2021 IEEE Congress on Evolutionary Computation (CEC), 2021.
Published in IEEE Transactions on Evolutionary Computation, 2021
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Recommended citation: Thiago Rios, Bas Stein, Thomas B{\"a}ck, Bernhard Sendhoff, Stefan Menzel, "Multitask Shape Optimization Using a 3-D Point Cloud Autoencoder as Unified Representation." IEEE Transactions on Evolutionary Computation, 2021.
Published in International Conference on Machine Learning, Optimization, and Data Science. Springer, 2021
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Recommended citation: Gideon Hanse, Roy Winter, Bas Stein, Thomas B{\"a}ck, "Optimally weighted ensembles for efficient multi-objective optimization." International Conference on Machine Learning, Optimization, and Data Science. Springer, 2021.
Published in 2021 International Conference on 3D Vision (3DV), 2021
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Recommended citation: Thiago Rios, Bas Van, Thomas B{\"a}ck, Bernhard Sendhoff, Stefan Menzel, "Point2FFD: learning shape representations of simulation-ready 3D models for engineering design optimization." 2021 International Conference on 3D Vision (3DV), 2021.
Published in Procedia Computer Science, 2021
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Recommended citation: Alexander Zeiser, Bas Stein, Thomas B{\"a}ck, "Requirements towards optimizing analytics in industrial processes." Procedia Computer Science, 2021.
Published in Evolutionary Multi-Criterion Optimization: 11th International Conference, EMO 2021, Shenzhen, China, March 28--31, 2021, Proceedings 11, 2021
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Recommended citation: Roy Winter, Bas Stein, Thomas B{\"a}ck, "Samo-cobra: A fast surrogate assisted constrained multi-objective optimization algorithm." Evolutionary Multi-Criterion Optimization: 11th International Conference, EMO 2021, Shenzhen, China, March 28--31, 2021, Proceedings 11, 2021.
Published in COMPIT'21, 2021
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Recommended citation: R Winter, B Stein, THW B{\"a}ck, V Bertram, "Ship design performance and cost optimization with machine learning." COMPIT'21, 2021.
Published in 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021
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Recommended citation: Koen Ponse, Anna Kononova, Maria Loleyt, Bas Van, "Using Machine Learning to Detect Rotational Symmetries from Reflectional Symmetries in 2D Images." 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021.
Published in IEEE Access, 2022
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Recommended citation: Bas Van, Elena Raponi, Zahra Sadeghi, Niek Bouman, Roeland Van, Thomas B{\"a}ck, "A comparison of global sensitivity analysis methods for explainable AI with an application in genomic prediction." IEEE Access, 2022.
Published in PHM Society European Conference, 2022
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Recommended citation: Marios Kefalas, Bas Stein, Mitra Baratchi, Asteris Apostolidis, Thomas B{\"a}ck, "An end-to-end pipeline for uncertainty quantification and remaining useful life estimation: An application on aircraft engines." PHM Society European Conference, 2022.
Published in Differential Evolution: From Theory to Practice, 2022
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Recommended citation: Diederick Vermetten, Bas Stein, Anna Kononova, Fabio Caraffini, "Analysis of structural bias in differential evolution configurations." Differential Evolution: From Theory to Practice, 2022.
Published in IEEE Transactions on Evolutionary Computation, 2022
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Recommended citation: Diederick Vermetten, Bas Stein, Fabio Caraffini, Leandro Minku, Anna Kononova, "BIAS: a toolbox for benchmarking structural bias in the continuous domain." IEEE Transactions on Evolutionary Computation, 2022.
Published in Memetic Computing, 2022
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Recommended citation: Roy Winter, Philip Bronkhorst, Bas Stein, Thomas B{\"a}ck, "Constrained multi-objective optimization with a limited budget of function evaluations." Memetic Computing, 2022.
Published in Journal of Aerospace Information Systems, 2022
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Recommended citation: Marios Kefalas, Juan Santiago, Asteris Apostolidis, Dirk Van, Bas Stein, Thomas B{\"a}ck, "Explainable artificial intelligence for exhaust gas temperature of turbofan engines." Journal of Aerospace Information Systems, 2022.
Published in Journal of Open Source Software, 2022
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Recommended citation: Bas Van, Elena Raponi, "GSAreport: Easy to Use Global Sensitivity Reporting." Journal of Open Source Software, 2022.
Published in Proceedings of the Genetic and Evolutionary Computation Conference, 2022
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Recommended citation: Fu Long, Bas Stein, Moritz Frenzel, Peter Krause, Markus Gitterle, Thomas B{\"a}ck, "Learning the characteristics of engineering optimization problems with applications in automotive crash." Proceedings of the Genetic and Evolutionary Computation Conference, 2022.
Published in Proceedings of the Genetic and Evolutionary Computation Conference, 2022
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Recommended citation: Roy Winter, Bas Stein, Thomas B{\"a}ck, "Multi-point acquisition function for constraint parallel efficient multi-objective optimization." Proceedings of the Genetic and Evolutionary Computation Conference, 2022.
Published in Preprint on arXiv preprint arXiv:2212.06438, 2022
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Recommended citation: Qi Huang, Roy Winter, Bas Stein, Thomas B{\"a}ck, Anna Kononova, "Multi-surrogate Assisted Efficient Global Optimization for Discrete Problems." Preprint on arXiv preprint arXiv:2212.06438, 2022.
Published in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2022
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Recommended citation: Diederick Vermetten, Fabio Caraffini, Bas Stein, Anna Kononova, "Using structural bias to analyse the behaviour of modular CMA-ES." Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2022.
Published in at-Automatisierungstechnik, 2023
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Recommended citation: Alexander Zeiser, Bekir {\"O}zcan, Christoph Kracke, Bas Stein, Thomas B{\"a}ck, "A data-centric approach to anomaly detection in layer-based additive manufacturing." at-Automatisierungstechnik, 2023.
Published in International Conference on the Applications of Evolutionary Computation (Part of EvoStar), 2023
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Recommended citation: Fu Long, Diederick Vermetten, Bas Stein, Anna Kononova, "BBOB Instance Analysis: Landscape Properties and Algorithm Performance Across Problem Instances." International Conference on the Applications of Evolutionary Computation (Part of EvoStar), 2023.
Published in Preprint on arXiv preprint arXiv:2305.15245, 2023
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Recommended citation: Fu Long, Diederick Vermetten, Anna Kononova, Roman Kalkreuth, Kaifeng Yang, Thomas B{\"a}ck, Niki Stein, "Challenges of ELA-guided Function Evolution using Genetic Programming." Preprint on arXiv preprint arXiv:2305.15245, 2023.
Published in Preprint on arXiv preprint arXiv:2304.01869, 2023
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Recommended citation: Bas Stein, Diederick Vermetten, Fabio Caraffini, Anna Kononova, "Deep-BIAS: Detecting Structural Bias using Explainable AI." Preprint on arXiv preprint arXiv:2304.01869, 2023.
Published in Preprint on arXiv preprint arXiv:2304.01219, 2023
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Recommended citation: Bas Stein, Fu Long, Moritz Frenzel, Peter Krause, Markus Gitterle, Thomas B{\"a}ck, "DoE2Vec: Deep-learning Based Features for Exploratory Landscape Analysis." Preprint on arXiv preprint arXiv:2304.01219, 2023.
Published in Computers in Industry, 2023
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Recommended citation: Alexander Zeiser, Bekir {\"O}zcan, Bas Stein, Thomas B{\"a}ck, "Evaluation of deep unsupervised anomaly detection methods with a data-centric approach for on-line inspection." Computers in Industry, 2023.
Published in Evolutionary Computation, 2023
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Recommended citation: Thomas B{\"a}ck, Anna Kononova, Bas Stein, Hao Wang, Kirill Antonov, Roman Kalkreuth, Jacob Nobel, Diederick Vermetten, Roy Winter, Furong Ye, "Evolutionary Algorithms for Parameter Optimization—Thirty Years Later." Evolutionary Computation, 2023.
Published in Preprint on arXiv preprint arXiv:2306.02985, 2023
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Recommended citation: Kirill Antonov, Anna Kononova, Thomas B{\"a}ck, Niki Stein, "Representation-agnostic distance-driven perturbation for optimizing ill-conditioned problems." Preprint on arXiv preprint arXiv:2306.02985, 2023.
Published in Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 2023
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Recommended citation: Kirill Antonov, Anna Kononova, Thomas B{\"a}ck, Niki Stein, "Curing ill-Conditionality via Representation-Agnostic Distance-Driven Perturbations." Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 2023.
Published in ECTA 2023, 2023
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Recommended citation: SL Thomson, N van Stein, D van den Berg, C van Leeuwen, "The Opaque Nature of Intelligence and the Pursuit of Explainable AI." ECTA 2023 proceedings, 2023.
Published in Preprint on arXiv preprint arXiv:2402.06299, 2024
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Recommended citation: Kirill Antonov, Roman Kalkreuth, Kaifeng Yang, Thomas B{\"a}ck, Niki Stein, Anna Kononova, "A Functional Analysis Approach to Symbolic Regression." Preprint on arXiv preprint arXiv:2402.06299, 2024.
Published in Preprint on arXiv preprint arXiv:2401.17842, 2024
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Recommended citation: Niki Stein, Diederick Vermetten, Anna Kononova, Thomas B{\"a}ck, "Explainable Benchmarking for Iterative Optimization Heuristics." Preprint on arXiv preprint arXiv:2401.17842, 2024.
Published in Swarm and Evolutionary Computation, 2024
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Recommended citation: Roy Winter, Bas Milatz, Julian Blank, Niki Stein, Thomas B{\"a}ck, Kalyanmoy Deb, "Parallel multi-objective optimization for expensive and inexpensive objectives and constraints." Swarm and Evolutionary Computation, 2024.
Published in Preprint on arXiv preprint arXiv:2402.01343, 2024
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Recommended citation: Qi Huang, Wei Chen, Thomas B{\"a}ck, Niki Stein, "Shapelet-based Model-agnostic Counterfactual Local Explanations for Time Series Classification." Preprint on arXiv preprint arXiv:2402.01343, 2024.
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Invited talk at the CWI, Amsterdam
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In business and academia we are continuously trying to model and analyze complex processes in order to gain insight and optimize. One of the most popular modeling algorithms is Kriging, or Gaussian Processes. A major bottleneck with Kriging is the amount of processing time of at least O(n3) and memory required O(n2) when applying this algorithm on medium to big data sets. With big data sets, that are more and more available these days, Kriging is not computationally feasible. As a solution to this problem we introduce a hybrid approach in which a number of Kriging models built on disjoint subsets of the data are properly weighted for the predictions. The proposed model is both in processing time and memory much more efficient than standard Global Kriging and performs equally well in terms of accuracy. The proposed algorithm is better scalable, and well suited for parallelization.
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Invited talk for the PhD Seminar at LIACS, Leiden University
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Invited lecture at the Data Science course, LIACS, Leiden University
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Invited talk for thhe PhH Colloquium with Math and Informatics
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Invited talk for the ECOLE program, learning to optimize.
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Invited talk at Tata Steel, about using deep learning to discover steel surface defects.
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Invited talk for the Summer school for Early Stage Researchers. This summer school takes place within the EU project Experience-based Computation: Learning to Optimise (ECOLE), which investigates novel synergies between nature-inspired optimisation and machine learning to address key challenges faced by the European industry.
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Invited presentation at SAILS (online)
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Invited presentation at BMW headquarters
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Multi-channel time-series classification is a challenging task. With sometimes thousands of sensors available for real-wolrd applications, it is a daunting and difficult task to select which channels to include and which not.
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The optimization of real-world engineering problems can be a challenging task, due to the limited understanding of problem characteristics and the high computational cost of objectives and constraints. This study proposes an AI-assisted optimization pipeline that addresses these challenges by using proxy functions in order to select and optimize an optimization algorithm and its hyper-parameters. It thereby significantly accelerates the optimization process on the real (expensive) problem. To obtain such proxy functions Exploratory Landscape Analysis (ELA) features are used to characterize the problem’s landscape. The ELA features are then used to identify an artificial function that replicates the original problem’s properties.
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🚢⚙️ Exciting News: “Computers Don’t Byte” Podcast by LIACS! 🎙️
Course, Bsc Computer Science, Leiden University, 2022
Data Science places data mining, machine learning and statistics in context, both experimentally and socially. If you want to correctly deploy data mining techniques, you must be able to translate a (broadly formulated) question by a customer or a co-worker into an experimental set-up, to make the right choices for the methods you use, and to be able to process the data in the right form to apply those methods. After performing your experiments, you should not only be able to evaluate the results but also interpret and translate it back to the original question (e.g. by visualization). Socially, data science is of great importance because the media simplify many data-driven results and statistical research, often making mistakes. Thus, a lot of nonsense comes down on us and it is up to you, the data scientists of the future, to recognize, explain and correct that nonsense. This course is a combination of lectures and practical sessions, in which you take a hands-on approach to solving real-world data science problems.
Course, Msc Computer Science, Leiden University, 2022
The Master Class for Computer Science students takes place every second week and is mandatory for all students who are in their second year, i.e., in their research year (Specialisation courses and the Master’s Thesis Research Project). The Master Class aims at stimulating active interaction of students with their classmates, discussing open problems, issues, etc., and helping students to stay on track. Each student is asked to give a brief presentation in the Master Class about their Master’s Thesis Research Project. About the research topic and goals, the status and (expected) results. In addition, we will discuss topics such as the structure of a Master’s Thesis, writing scientific publications, presenting a scientific paper, time management and other soft skills. In addition to these there are a number of guest lectures from companies, PhD students and entrepeneurs to prepare you for the job market after you graduate.