Bas van Stein

Publications

• A Novel Uncertainty Quantification Method for Efficient Global Optimization
• Data driven modeling & optimization of industrial processes
• SAMO-COBRA: A Fast Surrogate Assisted Constrained Multi-objective Optimization Algorithm
• Improving NSGA-III for flexible job shop scheduling using automatic configuration, smart initialization and local search
• Neural Network Design: Learning from Neural Architecture Search
• A Tailored NSGA-III for Multi-objective Flexible Job Shop Scheduling
• Feature visualization for 3d point cloud autoencoders
• Back To Meshes: Optimal Simulation-ready Mesh Prototypes For Autoencoder-based 3D Car Point Clouds
• Algorithm configuration data mining for CMA evolution strategies
• Designing Ships Using Constrained Multi-objective Efficient Global Optimization
• Analysis and Visualization of Missing Value Patterns
• Time complexity reduction in efficient global optimization using cluster kriging
• An Incremental Algorithm for Repairing Training Sets with Missing Values
• A framework for evaluating meta-models for simulation-based optimisation
• Cluster-based Kriging Approximation Algorithms for Complexity Reduction
• Algorithm configuration data mining for CMA evolution strategies
• An Incremental Algorithm for Repairing Training Sets with Missing Values
• Time complexity reduction in efficient global optimization using cluster kriging
• Analysis and Visualization of Missing Value Patterns
• Fuzzy clustering for Optimally Weighted Cluster Kriging
• Optimally weighted cluster kriging for big data regression
• Fuzzy clustering for optimally weighted cluster kriging
• Towards Data Driven Process Control in Manufacturing Car Body Parts
• Local subspace-based outlier detection using global neighbourhoods
• A New Acquisition Function for Bayesian optimization based on the moment-generating function
• A Multi-Method Simulation of a High-Frequency Bus Line
• Optimally Weighted Cluster Kriging for Big Data Regression
• A Framework for Evaluating Meta-models for Simulation-based Optimisation
• A New Approach Towards the Combined Algorithm Selection and Hyper-parameter Optimization Problem
• Automatic Configuration of Deep Neural Networks with EGO
• Scalability of Learning Tasks on 3D CAE Models Using Point Cloud Autoencoders
• On the Efficiency of a Point Cloud Autoencoder as a Geometric Representation for Shape Optimization
• Fitness Landscape Analysis of NK Landscapes and Vehicle Routing Problems by Expanded Barrier Trees
• Local Subspace-Based Outlier Detection using Global Neighbourhoods
• Towards Data Driven Process Control in Manufacturing Car Body Parts
• Automatic Configuration of Deep Neural Networks with Parallel Efficient Global Optimization
• BIAS: A Toolbox for Benchmarking Structural Bias in the Continuous Domain
• BIAS: A Toolbox for Benchmarking Structural Bias in the Continuous Domain
• Ship design performance and cost optimization with machine learning
• Analysis of Structural Bias in Differential Evolution Configurations
• Using machine learning to detect rotational and local reflectional symmetries in 2D images
• Exploiting generative models for performance predictions of 3D car designs
• Emergence of structural bias in differential evolution
• Exploiting local geometric features in vehicle design optimization with 3D point cloud autoencoders
• Requirements towards optimizing analytics in industrial processes
• Multi-task shape optimization using a 3D point cloud autoencoder as unified representation
• Explainable Artificial Intelligence for Exhaust Gas Temperature of Turbofan Engines
• Optimally weighted ensembles for efficient multi-objective optimization
• Point2FFD
• Constrained Multi-Objective Optimization with a Limited Budget of Function Evaluations
• Multi-point acquisition function for constraint parallel efficient multi-objective optimization
• A Comparison of Global Sensitivity Analysis Methods for Explainable AI with an Application in Genomic Prediction
• Using structural bias to analyse the behaviour of modular CMA-ES
• A Comparison of Global Sensitivity Analysis Methods for Explainable AI With an Application in Genomic Prediction
• GSAreport: Easy to Use Global Sensitivity Reporting
• Learning the characteristics of engineering optimization problems with applications in automotive crash
• End-to-end pipeline for uncertainty quantification and remaining useful life estimation
• BBOB Instance Analysis: Landscape Properties and Algorithm Performance Across Problem Instances
• Using Machine Learning to Detect Rotational Symmetries from Reflectional Symmetries in 2D Images
• FOREWORD
• A tailored NSGA-III instantiation for flexible job shop scheduling?
• Cluster-based kriging approximation algorithms for complexity reduction
• MULTI-SURROGATE ASSISTED EFFICIENT GLOBAL OPTIMIZATION FOR DISCRETE PROBLEMS
• DOE2VEC: DEEP-LEARNING BASED FEATURES FOR EXPLORATORY LANDSCAPE ANALYSIS
• Fitness landscape analysis of NK landscapes and Vehicle Routing problems by expanded Barrier trees
• Multi-surrogate Assisted Efficient Global Optimization for Discrete Problems
• Towards data driven process control in manufacturing car body parts
• BBOB Instance Analysis: Landscape Properties and Algorithm Performance across Problem Instances
• Evaluation of deep unsupervised anomaly detection methods with a data-centric approach for on-line inspection
• Ein datenzentrierter Ansatz für Anomaliedetektion in schichtbasierten additiven Fertigungsverfahren,A data-centric approach to anomaly detection in layer-based additive manufacturing
• A DATA-CENTRIC APPROACH TO ANOMALY DETECTION IN LAYER-BASED ADDITIVE MANUFACTURING
• DEEP-BIAS: DETECTING STRUCTURAL BIAS USING EXPLAINABLE AI
• Neural network design
• DoE2Vec: Deep-learning Based Features for Exploratory Landscape Analysis
• Deep BIAS: Detecting Structural Bias using Explainable AI
• Curing ill-Conditionality via Representation-Agnostic Distance-Driven Perturbations
• The opaque nature of intelligence and the pursuit of explainable AI
• AI for expensive optimization problems in industry
• Clustering-based domain-incremental learning
• Evolutionary algorithms for parameter optimization
• Explainable AI for ship design analysis with AIS and static ship data
• Parallel multi-objective optimization for expensive and inexpensive objectives and constraints