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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.

Optimally weighted cluster kriging for big data regression

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.

A framework for evaluating meta-models for simulation-based optimisation

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.

An incremental algorithm for repairing training sets with missing values

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.

Analysis and visualization of missing value patterns

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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.

Fuzzy clustering for optimally weighted cluster kriging

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.

Local subspace-based outlier detection using global neighbourhoods

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.

Towards data driven process control in manufacturing car body parts

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.

A multi-method simulation of a high-frequency bus line

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.

A new acquisition function for Bayesian optimization based on the moment-generating function

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.

Algorithm configuration data mining for cma evolution strategies

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.

Time complexity reduction in efficient global optimization using cluster kriging

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.

A novel uncertainty quantification method for efficient global optimization

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.

A new approach towards the combined algorithm selection and hyper-parameter optimization problem

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.

Automatic configuration of deep neural networks with parallel efficient global optimization

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.

Designing ships using constrained multi-objective efficient global optimization

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.

On the efficiency of a point cloud autoencoder as a geometric representation for shape optimization

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.

Scalability of learning tasks on 3D CAE models using point cloud autoencoders

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.

A tailored NSGA-III for multi-objective flexible job shop scheduling

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.

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

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.

Cluster-based Kriging approximation algorithms for complexity reduction

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.

Feature visualization for 3D point cloud autoencoders

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.

Improving NSGA-III for flexible job shop scheduling using automatic configuration, smart initialization and local search

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.

Neural network design: learning from neural architecture search

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.

Emergence of structural bias in differential evolution

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.

Exploiting generative models for performance predictions of 3D car designs

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.

Exploiting local geometric features in vehicle design optimization with 3D point cloud autoencoders

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.

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

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.

Optimally weighted ensembles for efficient multi-objective optimization

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.

Point2FFD: learning shape representations of simulation-ready 3D models for engineering design optimization

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.

Requirements towards optimizing analytics in industrial processes

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.

Samo-cobra: A fast surrogate assisted constrained multi-objective optimization algorithm

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.

Ship design performance and cost optimization with machine learning

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.

Using Machine Learning to Detect Rotational Symmetries from Reflectional Symmetries in 2D Images

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.

A comparison of global sensitivity analysis methods for explainable AI with an application in genomic prediction

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.

An end-to-end pipeline for uncertainty quantification and remaining useful life estimation: An application on aircraft engines

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.

Analysis of structural bias in differential evolution configurations

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.

BIAS: a toolbox for benchmarking structural bias in the continuous domain

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.

Constrained multi-objective optimization with a limited budget of function evaluations

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.

Explainable artificial intelligence for exhaust gas temperature of turbofan engines

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.

GSAreport: Easy to Use Global Sensitivity Reporting

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.

Learning the characteristics of engineering optimization problems with applications in automotive crash

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.

Multi-point acquisition function for constraint parallel efficient multi-objective optimization

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.

Multi-surrogate Assisted Efficient Global Optimization for Discrete Problems

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.

Using structural bias to analyse the behaviour of modular CMA-ES

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.

A data-centric approach to anomaly detection in layer-based additive manufacturing

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.

BBOB Instance Analysis: Landscape Properties and Algorithm Performance Across Problem Instances

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.

Challenges of ELA-guided Function Evolution using Genetic Programming

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.

Curing ill-Conditionality via Representation-Agnostic Distance-Driven Perturbations

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.

Deep-BIAS: Detecting Structural Bias using Explainable AI

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.

DoE2Vec: Deep-learning Based Features for Exploratory Landscape Analysis

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.

Evaluation of deep unsupervised anomaly detection methods with a data-centric approach for on-line inspection

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.

Evolutionary Algorithms for Parameter Optimization—Thirty Years Later

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.

New solutions to Cooke triplet problem via analysis of attraction basins

Published in Digital Optical Technologies 2023, 2023

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Recommended citation: Kirill Antonov, Tiago Botari, Teuss Tukker, Thomas B{\"a}ck, Niki Stein, Anna Kononova, "New solutions to Cooke triplet problem via analysis of attraction basins." Digital Optical Technologies 2023, 2023.

Real-World Optimization Benchmark from Vehicle Dynamics: Specification of Problems in 2D and Methodology for Transferring (Meta-) Optimized Algorithm Parameters.

Published in IJCCI, 2023

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Recommended citation: Andr{\'e} Thomaser, Marc-Eric Vogt, Thomas B{\"a}ck, Anna Kononova, N Stein, F Marcelloni, HK Lam, M Cottrell, J Filipe, "Real-World Optimization Benchmark from Vehicle Dynamics: Specification of Problems in 2D and Methodology for Transferring (Meta-) Optimized Algorithm Parameters.." IJCCI, 2023.

Representation-agnostic distance-driven perturbation for optimizing ill-conditioned problems

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.

Curing ill-Conditionality via Representation-Agnostic Distance-Driven Perturbations

Published in Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 2023

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The Opaque Nature of Intelligence and the Pursuit of Explainable AI

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.

A Corridor Model Evolutionary Algorithm for Fast Converging Green Vehicle Routing Problem

Published in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2024

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Recommended citation: Ananta Shahane, Niki Van, Yingjie Fan, "A Corridor Model Evolutionary Algorithm for Fast Converging Green Vehicle Routing Problem." Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2024.

A Critical Analysis of Raven Roost Optimization

Published in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2024

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Recommended citation: Martijn Halsema, Diederick Vermetten, Thomas B{\"a}ck, Niki Van, "A Critical Analysis of Raven Roost Optimization." Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2024.

A Functional Analysis Approach to Symbolic Regression

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.

A deep dive into effects of structural bias on cma-es performance along affine trajectories

Published in International Conference on Parallel Problem Solving from Nature, 2024

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Recommended citation: Niki Stein, Sarah Thomson, Anna Kononova, "A deep dive into effects of structural bias on cma-es performance along affine trajectories." International Conference on Parallel Problem Solving from Nature, 2024.

A functional analysis approach to symbolic regression

Published in Proceedings of the Genetic and Evolutionary Computation Conference, 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." Proceedings of the Genetic and Evolutionary Computation Conference, 2024.

A survey of meta-features used for automated selection of algorithms for black-box single-objective continuous optimization

Published in arXiv preprint arXiv:2406.06629, 2024

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Recommended citation: Gjorgjina Cenikj, Ana Nikolikj, Ga{\v{s}}per Petelin, Niki Stein, Carola Doerr, Tome Eftimov, "A survey of meta-features used for automated selection of algorithms for black-box single-objective continuous optimization." arXiv preprint arXiv:2406.06629, 2024.

ACO-NSGAII: A Novel Metaheuristics for Bi-Objective Electric Vehicle Routing Problems

Published in 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), 2024

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Recommended citation: Suzan Al-Nassar, Niki Stein, Yingjie Fan, "ACO-NSGAII: A Novel Metaheuristics for Bi-Objective Electric Vehicle Routing Problems." 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), 2024.

Adaptive model pruning in federated learning through loss exploration

Published in 2nd Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization (WANT@ ICML 2024), 2024

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Recommended citation: Christian Interno, Elena Raponi, Niki Stein, Thomas B{\"a}ck, Markus Olhofer, Yaochu Jin, Barbara Hammer, "Adaptive model pruning in federated learning through loss exploration." 2nd Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization (WANT@ ICML 2024), 2024.

Automated Federated Learning via Informed Pruning

Published in ArXiv, vol. abs/2405.10271, 2024

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Recommended citation: Christian Intern{\`o}, Elena Raponi, Niki Stein, Thomas B{\"a}ck, Markus Olhofer, Yaochu Jin, Barbara Hammer, "Automated Federated Learning via Informed Pruning." ArXiv, vol. abs/2405.10271, 2024.

Beyond the veil of similarity: Quantifying semantic continuity in explainable AI

Published in World Conference on Explainable Artificial Intelligence, 2024

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Recommended citation: Qi Huang, Emanuele Mezzi, Osman Mutlu, Miltiadis Kofinas, Vidya Prasad, Shadnan Khan, Elena Ranguelova, Niki Stein, "Beyond the veil of similarity: Quantifying semantic continuity in explainable AI." World Conference on Explainable Artificial Intelligence, 2024.

Development of a photonic crystal spectrometer for greenhouse gas measurements

Published in arXiv preprint arXiv:2411.02056, 2024

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Recommended citation: Marijn Siemonsa, Martijn Veen, Irina Malysheva, Johannes Algera, Stefan Philippi, Kirill Antonov, Niki Stein, J{\'e}r{\^o}me Loicq, Nandini Bhattacharya, Ren{\'e} Berlich, "Development of a photonic crystal spectrometer for greenhouse gas measurements." arXiv preprint arXiv:2411.02056, 2024.

EconoJax: A Fast & Scalable Economic Simulation in Jax

Published in arXiv preprint arXiv:2410.22165, 2024

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Recommended citation: Koen Ponse, Aske Plaat, Niki Stein, Thomas Moerland, "EconoJax: A Fast & Scalable Economic Simulation in Jax." arXiv preprint arXiv:2410.22165, 2024.

Evolutionary computation and explainable AI: A roadmap to understandable intelligent systems

Published in IEEE Transactions on Evolutionary Computation, 2024

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Recommended citation: Ryan Zhou, Jaume Bacardit, Alexander Brownlee, Stefano Cagnoni, Martin Fyvie, Giovanni Iacca, John McCall, Niki Stein, David Walker, Ting Hu, "Evolutionary computation and explainable AI: A roadmap to understandable intelligent systems." IEEE Transactions on Evolutionary Computation, 2024.

Explainable Benchmarking for Iterative Optimization Heuristics

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.

Explainable benchmarking for iterative optimization heuristics (2024)

Published in 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 (2024)." arXiv preprint arXiv:2401.17842, 2024.

Federated Hybrid Model Pruning through Loss Landscape Exploration

Published in arXiv preprint arXiv:2405.10271, 2024

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Recommended citation: Christian Intern{\`o}, Elena Raponi, Niki Stein, Thomas B{\"a}ck, Markus Olhofer, Yaochu Jin, Barbara Hammer, "Federated Hybrid Model Pruning through Loss Landscape Exploration." arXiv preprint arXiv:2405.10271, 2024.

Generating cheap representative functions for expensive automotive crashworthiness optimization

Published in ACM Transactions on Evolutionary Learning and Optimization, 2024

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Recommended citation: Fu Long, Bas Stein, Moritz Frenzel, Peter Krause, Markus Gitterle, Thomas B{\"a}ck, "Generating cheap representative functions for expensive automotive crashworthiness optimization." ACM Transactions on Evolutionary Learning and Optimization, 2024.

In-the-loop hyper-parameter optimization for llm-based automated design of heuristics

Published in ACM Transactions on Evolutionary Learning, 2024

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Recommended citation: Niki Stein, Diederick Vermetten, Thomas B{\"a}ck, "In-the-loop hyper-parameter optimization for llm-based automated design of heuristics." ACM Transactions on Evolutionary Learning, 2024.

Landscape analysis based vs. domain-specific optimization for engineering design applications: a clear case

Published in 2024 IEEE Conference on Artificial Intelligence (CAI), 2024

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Recommended citation: Roy Winter, Fu Long, Andre Thomaser, Thomas B{\"a}ck, Niki Stein, Anna Kononova, "Landscape analysis based vs. domain-specific optimization for engineering design applications: a clear case." 2024 IEEE Conference on Artificial Intelligence (CAI), 2024.

Landscape-aware automated algorithm configuration using multi-output mixed regression and classification

Published in International Conference on Parallel Problem Solving from Nature, 2024

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Recommended citation: Fu Long, Moritz Frenzel, Peter Krause, Markus Gitterle, Thomas B{\"a}ck, Niki Stein, "Landscape-aware automated algorithm configuration using multi-output mixed regression and classification." International Conference on Parallel Problem Solving from Nature, 2024.

Llamea: A large language model evolutionary algorithm for automatically generating metaheuristics

Published in IEEE Transactions on Evolutionary Computation, 2024

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Recommended citation: Niki Stein, Thomas B{\"a}ck, "Llamea: A large language model evolutionary algorithm for automatically generating metaheuristics." IEEE Transactions on Evolutionary Computation, 2024.

Parallel multi-objective optimization for expensive and inexpensive objectives and constraints

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.

Quality-diversity driven robust evolutionary optimization of optical designs

Published in Computational Optics 2024, 2024

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Recommended citation: Kirill Antonov, Teus Tukker, Tiago Botari, Thomas B{\"a}ck, Anna Kononova, Niki Stein, "Quality-diversity driven robust evolutionary optimization of optical designs." Computational Optics 2024, 2024.

Reasoning with large language models, a survey

Published in arXiv preprint arXiv:2407.11511, 2024

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Recommended citation: Aske Plaat, Annie Wong, Suzan Verberne, Joost Broekens, Niki Stein, Thomas Back, "Reasoning with large language models, a survey." arXiv preprint arXiv:2407.11511, 2024.

Selection of filters for photonic crystal spectrometer using domain-aware evolutionary algorithms

Published in arXiv preprint arXiv:2410.13657, 2024

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Recommended citation: Kirill Antonov, Marijn Siemons, Niki Stein, Thomas B{\"a}ck, Ralf Kohlhaas, Anna Kononova, "Selection of filters for photonic crystal spectrometer using domain-aware evolutionary algorithms." arXiv preprint arXiv:2410.13657, 2024.

Shapelet-based Model-agnostic Counterfactual Local Explanations for Time Series Classification

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.

Shapelet-based model-agnostic counterfactual local explanations for time series classification

Published in 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." arXiv preprint arXiv:2402.01343, 2024.

TX-Gen: multi-objective optimization for sparse counterfactual explanations for time-series classification

Published in arxiv, 2024

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Recommended citation: Q Huang, S Kitharidis, THW B{\"a}ck, N Stein, "TX-Gen: multi-objective optimization for sparse counterfactual explanations for time-series classification." arxiv, 2024.

Towards Fairness in Machine Learning: Balancing Racially Imbalanced Datasets Through Data Augmentation and Generative AI

Published in IJCCI, 2024

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Recommended citation: Anthonie Schaap, Sofoklis Kitharidis, Niki Stein, "Towards Fairness in Machine Learning: Balancing Racially Imbalanced Datasets Through Data Augmentation and Generative AI." IJCCI, 2024.

Agentic large language models, a survey

Published in arXiv preprint arXiv:2503.23037, 2025

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Recommended citation: Aske Plaat, Max Duijn, Niki Stein, Mike Preuss, Peter Putten, Kees Batenburg, "Agentic large language models, a survey." arXiv preprint arXiv:2503.23037, 2025.

An Introduction to the Crossroads of Explainable Artificial Intelligence and Evolutionary Computation

Published in Explainable AI for Evolutionary Computation, 2025

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Recommended citation: Niki Stein, Anna Kononova, "An Introduction to the Crossroads of Explainable Artificial Intelligence and Evolutionary Computation." Explainable AI for Evolutionary Computation, 2025.

Automated Algorithm Design for Auto-Tuning Optimizers

Published in arXiv preprint arXiv:2510.17899, 2025

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Recommended citation: Floris-Jan Willemsen, Niki Stein, Ben Werkhoven, "Automated Algorithm Design for Auto-Tuning Optimizers." arXiv preprint arXiv:2510.17899, 2025.

BLADE: Benchmark suite for LLM-driven Automated Design and Evolution of iterative optimisation heuristics

Published in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2025

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Recommended citation: Niki Stein, Anna V., Haoran Yin, Thomas B{\"a}ck, "BLADE: Benchmark suite for LLM-driven Automated Design and Evolution of iterative optimisation heuristics." Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2025.

Behaviour space analysis of llm-driven meta-heuristic discovery

Published in arXiv preprint arXiv:2507.03605, 2025

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Recommended citation: Niki Stein, Haoran Yin, Anna Kononova, Thomas B{\"a}ck, Gabriela Ochoa, "Behaviour space analysis of llm-driven meta-heuristic discovery." arXiv preprint arXiv:2507.03605, 2025.

Benchmarking that Matters: Rethinking Benchmarking for Practical Impact

Published in arXiv preprint arXiv:2511.12264, 2025

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Recommended citation: Anna Kononova, Niki Stein, Olaf Mersmann, Thomas B{\"a}ck, Thomas Bartz-Beielstein, Tobias Glasmachers, Michael Hellwig, Sebastian Krey, Jakub K{\uu}dela, Boris Naujoks, "Benchmarking that Matters: Rethinking Benchmarking for Practical Impact." arXiv preprint arXiv:2511.12264, 2025.

CoComposer: LLM Multi-agent Collaborative Music Composition

Published in arXiv preprint arXiv:2509.00132, 2025

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Recommended citation: Peiwen Xing, Aske Plaat, Niki Stein, "CoComposer: LLM Multi-agent Collaborative Music Composition." arXiv preprint arXiv:2509.00132, 2025.

Code evolution graphs: Understanding large language model driven design of algorithms

Published in Proceedings of the Genetic and Evolutionary Computation Conference, 2025

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Recommended citation: Niki Stein, Anna V., Lars Kotthoff, Thomas B{\"a}ck, "Code evolution graphs: Understanding large language model driven design of algorithms." Proceedings of the Genetic and Evolutionary Computation Conference, 2025.

Controlling the mutation in large language models for the efficient evolution of algorithms

Published in International Conference on the Applications of Evolutionary Computation (Part of EvoStar), 2025

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Recommended citation: Haoran Yin, Anna Kononova, Thomas B{\"a}ck, Niki Stein, "Controlling the mutation in large language models for the efficient evolution of algorithms." International Conference on the Applications of Evolutionary Computation (Part of EvoStar), 2025.

Diffusion Models for Tabular Data: Challenges, Current Progress, and Future Directions

Published in arXiv preprint arXiv:2502.17119, 2025

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Recommended citation: Zhong Li, Qi Huang, Lincen Yang, Jiayang Shi, Zhao Yang, Niki Stein, Thomas B{\"a}ck, Matthijs Leeuwen, "Diffusion Models for Tabular Data: Challenges, Current Progress, and Future Directions." arXiv preprint arXiv:2502.17119, 2025.

EvoCAD: Evolutionary CAD Code Generation with Vision Language Models

Published in arXiv preprint arXiv:2510.11631, 2025

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Recommended citation: Tobias Preintner, Weixuan Yuan, Adrian K{\"o}nig, Thomas B{\"a}ck, Elena Raponi, Niki Stein, "EvoCAD: Evolutionary CAD Code Generation with Vision Language Models." arXiv preprint arXiv:2510.11631, 2025.

Evolutionary Computation and Large Language Models: A Survey of Methods, Synergies, and Applications

Published in arXiv preprint arXiv:2505.15741, 2025

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Recommended citation: Dikshit Chauhan, Bapi Dutta, Indu Bala, Niki Stein, Thomas B{\"a}ck, Anupam Yadav, "Evolutionary Computation and Large Language Models: A Survey of Methods, Synergies, and Applications." arXiv preprint arXiv:2505.15741, 2025.

Explainable benchmarking for iterative optimization heuristics

Published in ACM Transactions on Evolutionary Learning, 2025

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Recommended citation: Niki Stein, Diederick Vermetten, Anna V., Thomas B{\"a}ck, "Explainable benchmarking for iterative optimization heuristics." ACM Transactions on Evolutionary Learning, 2025.

From Performance to Understanding: A Vision for Explainable Automated Algorithm Design

Published in arXiv preprint arXiv:2511.16201, 2025

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Recommended citation: Niki Stein, Anna Kononova, Thomas B{\"a}ck, "From Performance to Understanding: A Vision for Explainable Automated Algorithm Design." arXiv preprint arXiv:2511.16201, 2025.

Global Sensitivity Analysis Is Not Always Beneficial for Evolutionary Computation: A Study in Engineering Design

Published in Explainable AI for Evolutionary Computation, 2025

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Recommended citation: Elena Raponi, Ivan Rodriguez, Niki Stein, "Global Sensitivity Analysis Is Not Always Beneficial for Evolutionary Computation: A Study in Engineering Design." Explainable AI for Evolutionary Computation, 2025.

How does chain of thought think? mechanistic interpretability of chain-of-thought reasoning with sparse autoencoding

Published in arXiv preprint arXiv:2507.22928, 2025

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Recommended citation: Xi Chen, Aske Plaat, Niki Stein, "How does chain of thought think? mechanistic interpretability of chain-of-thought reasoning with sparse autoencoding." arXiv preprint arXiv:2507.22928, 2025.

LLaMEA-BO: A Large Language Model Evolutionary Algorithm for Automatically Generating Bayesian Optimization Algorithms

Published in arXiv preprint arXiv:2505.21034, 2025

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Recommended citation: Wenhu Li, Niki Stein, Thomas B{\"a}ck, Elena Raponi, "LLaMEA-BO: A Large Language Model Evolutionary Algorithm for Automatically Generating Bayesian Optimization Algorithms." arXiv preprint arXiv:2505.21034, 2025.

Leveraging Lightweight Generators for Memory Efficient Continual Learning

Published in Intelligent Systems Conference, 2025

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Recommended citation: Christiaan Lamers, Ahmed Belbachir, Thomas B{\"a}ck, Niki Stein, "Leveraging Lightweight Generators for Memory Efficient Continual Learning." Intelligent Systems Conference, 2025.

Multi-step reasoning with large language models, a survey

Published in ACM Computing Surveys, 2025

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Recommended citation: Aske Plaat, Annie Wong, Suzan Verberne, Joost Broekens, Niki Van, "Multi-step reasoning with large language models, a survey." ACM Computing Surveys, 2025.

Neighborhood Adaptive Differential Evolution

Published in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2025

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Recommended citation: Niki Stein, "Neighborhood Adaptive Differential Evolution." Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2025.

Optimizing photonic structures with large language model driven algorithm discovery

Published in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2025

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Recommended citation: Haoran Yin, Anna Kononova, Thomas B{\"a}ck, Niki Stein, "Optimizing photonic structures with large language model driven algorithm discovery." Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2025.

Quality–diversity-driven robust evolutionary optimization of optical designs

Published in Optical Engineering, 2025

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Recommended citation: Kirill Antonov, Teus Tukker, Tiago Botari, Thomas B{\"a}ck, Anna Kononova, Niki Stein, "Quality--diversity-driven robust evolutionary optimization of optical designs." Optical Engineering, 2025.

REMAINING USEFUL LIFE IN COMPLEX MULTI-COMPONENT SYSTEMS: TAXONOMY, REVIEW, AND RESEARCH DIRECTIONS

Published in Authorea Preprints, 2025

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Recommended citation: Bernd Wagner, Qi Huang, Lucas Correia, Thomas B{\"a}ck, Anna Kononova, Niki Van, "REMAINING USEFUL LIFE IN COMPLEX MULTI-COMPONENT SYSTEMS: TAXONOMY, REVIEW, AND RESEARCH DIRECTIONS." Authorea Preprints, 2025.

Scalable, Explainable and Provably Robust Anomaly Detection with One-Step Flow Matching

Published in arXiv preprint arXiv:2510.18328, 2025

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Recommended citation: Zhong Li, Qi Huang, Yuxuan Zhu, Lincen Yang, Mohammad Amiri, Niki Stein, Matthijs Leeuwen, "Scalable, Explainable and Provably Robust Anomaly Detection with One-Step Flow Matching." arXiv preprint arXiv:2510.18328, 2025.

Stalling in Space: Attractor Analysis for any Algorithm

Published in International Conference on the Applications of Evolutionary Computation (Part of EvoStar), 2025

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Recommended citation: Sarah Thomson, Quentin Renau, Diederick Vermetten, Emma Hart, Niki Stein, Anna Kononova, "Stalling in Space: Attractor Analysis for any Algorithm." International Conference on the Applications of Evolutionary Computation (Part of EvoStar), 2025.

Structural bias in optimization algorithms

Published in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2025

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Recommended citation: Anna Kononova, Niki Stein, "Structural bias in optimization algorithms." Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2025.

Surrogate-based automated hyperparameter optimization for expensive automotive crashworthiness optimization

Published in Structural and Multidisciplinary Optimization, 2025

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Recommended citation: Fu Long, Niki Stein, Moritz Frenzel, Peter Krause, Markus Gitterle, Thomas B{\"a}ck, "Surrogate-based automated hyperparameter optimization for expensive automotive crashworthiness optimization." Structural and Multidisciplinary Optimization, 2025.

TeVAE: a variational autoencoder aproach for discrete online anomaly detection in variable-state multivariate time-series data

Published in Studies in Computational Intelligence, 2025

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Recommended citation: L Ferreira, JC Goos, P Klein, THW B{\"a}ck, AV Kononova, N Stein, C Wagner, JM Garibaldi, F Marcelloni, HK Lam, "TeVAE: a variational autoencoder aproach for discrete online anomaly detection in variable-state multivariate time-series data." Studies in Computational Intelligence, 2025.

The Synergy of Explainable AI and Evolutionary Computation: Real-World Applications

Published in Explainable AI for Evolutionary Computation, 2025

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Recommended citation: Niki Stein, Qi Huang, Elena Raponi, "The Synergy of Explainable AI and Evolutionary Computation: Real-World Applications." Explainable AI for Evolutionary Computation, 2025.

Towards a Deeper Understanding of Reasoning Capabilities in Large Language Models

Published in arXiv preprint arXiv:2505.10543, 2025

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Recommended citation: Annie Wong, Thomas B{\"a}ck, Aske Plaat, Niki Stein, Anna Kononova, "Towards a Deeper Understanding of Reasoning Capabilities in Large Language Models." arXiv preprint arXiv:2505.10543, 2025.

Visual Model Selection using Feature Importance Clusters in Fairness-Performance Similarity Optimized Space

Published in arXiv preprint arXiv:2510.22209, 2025

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Recommended citation: Sofoklis Kitharidis, Cor Veenman, Thomas B{\"a}ck, Niki Stein, "Visual Model Selection using Feature Importance Clusters in Fairness-Performance Similarity Optimized Space." arXiv preprint arXiv:2510.22209, 2025.

Why Are You Wrong? Counterfactual Explanations for Language Grounding with 3D Objects

Published in arXiv preprint arXiv:2505.06030, 2025

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Recommended citation: Tobias Preintner, Weixuan Yuan, Qi Huang, Adrian K{\"o}nig, Thomas B{\"a}ck, Elena Raponi, Niki Stein, "Why Are You Wrong? Counterfactual Explanations for Language Grounding with 3D Objects." arXiv preprint arXiv:2505.06030, 2025.

XAI for Benchmarking Black-Box Metaheuristics

Published in Explainable AI for Evolutionary Computation, 2025

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Recommended citation: Anna Kononova, Diederick Vermetten, Niki Stein, "XAI for Benchmarking Black-Box Metaheuristics." Explainable AI for Evolutionary Computation, 2025.

talks

Datamining & Apps

Published: March 10, 2015

Invited talk at the CWI, Amsterdam

Fuzzy Clustering for Optimally Weighted Cluster Kriging

Published: July 25, 2016

Invited talk for the PhD Seminar at LIACS, Leiden University

From Data to Knowledge to Optimization

Published: February 02, 2017

Invited talk for thhe PhH Colloquium with Math and Informatics

Deep Steel: Optimizing networks to discover defects on steel surface

Published: November 29, 2018

Invited talk for the ECOLE program, learning to optimize.

Deep Steel: Optimizing networks to discover defects on steel surface

Published: January 30, 2019

Invited talk at Tata Steel, about using deep learning to discover steel surface defects.

Deep Learning, A broad introduction

Published: July 30, 2019

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.

Automated Deep Learning

Published: March 11, 2020

Invited presentation at SAILS (online)

Neural Architecture Search VS Black Box Optimization

Published: November 22, 2021

Invited presentation at BMW headquarters

Computers Don’t Byte: XAI

Published: March 25, 2024

🚢⚙️ Exciting News: “Computers Don’t Byte” Podcast by LIACS! 🎙️

XAI in Industry

Published: August 01, 2024

Invited talk on applications of explainable AI in industry at the SAILS Lunch Time Seminar.

XAI for Predictive Maintenance

Published: September 01, 2024

Invited talk on explainable AI for predictive maintenance at Microsoft Netherlands.

XAI for Healthcare

Published: October 10, 2024

Invited talk on explainable AI for healthcare at VZI HealthTech 2024.

Explainable AI

Published: October 25, 2024

Invited talk on explainable AI at Alice and Eve 2024.

Evaluating XAI

Published: January 21, 2025

Invited talk on evaluating explainable AI at the XAI-NL meetup.

LLaMEA for Automated Algorithm Design

Published: February 20, 2025

Invited talk for the AToNIIC lecture series: https://altzanetos.com/atoniic/llamea-llm-ea-for-automatically-generating-metaheuristics/ (February 20, 2025, 15:00 CET).

XAI for Time Series

Published: March 01, 2025

Invited talk on explainable AI for time series at TU Delft.

Automated Algorithm Design

Published: June 10, 2025

Invited talk for the AutoML summer school in Tübingen, Germany (June 10–13, 2025).

Automated Algorithm Design

Published: July 31, 2025

Invited talk for the AutoML Seminar Series.

Explainable Automated Algorithm Design

Published: November 25, 2025

Invited keynote for the Auto-Learn SI workshop focusing on explainable approaches to automated algorithm design.

teaching

Data Science course

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.

Business Analytics (Minor)

Course, Bsc Computer Science, Leiden University, 2024

Business Analytics places data mining, visualisation, machine learning and statistics in a business context. If you want to correctly deploy data mining / AI 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) and communicate findings to management of colleagues. 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 in a business context.

Applied Data Science and Explainable AI

Course, Bsc Computer Science, Leiden University, 2025

Niki van Stein

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