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Multi-surrogate Assisted Efficient Global Optimization for Discrete Problems
Qi Huang;Roy De Winter;Bas Van Stein;Thomas Bäck;Anna V. Kononova
2022 IEEE Symposium Series on Computational Intelligence (SSCI)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
Decades of progress in simulation-based surrogate assisted optimization and unprecedented growth in computational power have enabled researchers and practitioners to optimize previously intractable complex engineering problems. This paper investigates the possible benefit of a concurrent utilization of multiple simulation-based surrogate models to solve complex discrete optimization problems. To f...Show More
BIAS: A Toolbox for Benchmarking Structural Bias in the Continuous Domain
Diederick Vermetten;Bas van Stein;Fabio Caraffini;Leandro L. Minku;Anna V. Kononova
Benchmarking heuristic algorithms is vital to understand under which conditions and on what kind of problems certain algorithms perform well. Most benchmarks are performance based, to test algorithm performance under a wide set of conditions. There is also resource- and behavior-based benchmarks to test the resource consumption and the behavior of algorithms. In this article, we propose a novel be...Show More
Multitask Shape Optimization Using a 3-D Point Cloud Autoencoder as Unified Representation
Thiago Rios;Bas van Stein;Thomas Bäck;Bernhard Sendhoff;Stefan Menzel
The choice of design representations, as of search operators, is central to the performance of evolutionary optimization algorithms, in particular, for multitask problems. The multitask approach pushes further the parallelization aspect of these algorithms by solving simultaneously multiple optimization tasks using a single population. During the search, the operators implicitly transfer knowledge...Show More
Using Machine Learning to Detect Rotational Symmetries from Reflectional Symmetries in 2D Images
2021 IEEE Symposium Series on Computational Intelligence (SSCI)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
Automated symmetry detection is still a difficult task in 2021. However, it has applications in computer vision, and it also plays an important part in understanding art. This paper focuses on aiding the latter by comparing different state-of-the-art automated symmetry detection algorithms. For one of such algorithms aimed at reflectional symmetries, we propose postprocessing improvements to find ...Show More
Point2FFD: Learning Shape Representations of Simulation-Ready 3D Models for Engineering Design Optimization
Thiago Rios;Bas Van Stein;Thomas Bäck;Bernhard Sendhoff;Stefan Menzel
2021 International Conference on 3D Vision (3DV)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
Methods for learning on 3D point clouds became ubiquitous due to the popularization of 3D scanning technology and advances of machine learning techniques. Among these methods, point-based deep neural networks have been utilized to explore 3D designs in optimization tasks. However, engineering computer simulations require high-quality meshed models, which are challenging to automatically generate f...Show More
Exploiting Local Geometric Features in Vehicle Design Optimization with 3D Point Cloud Autoencoders
Thiago Rios;Bas van Stein;Patricia Wollstadt;Thomas Bäck;Bernhard Sendhoff;Stefan Menzel
2021 IEEE Congress on Evolutionary Computation (CEC)
Year: 2021 | Conference Paper |
Cited by: Papers (5)
Methods for learning and compressing high-dimensional data allow designers to generate novel and low-dimensional design representations for shape optimization problems. By using compact design spaces, global optimization algorithms require less function evaluations to characterize the problem landscape. Furthermore, data-driven representations are often domain-agnostic and independent of the user ...Show More
Back To Meshes: Optimal Simulation-ready Mesh Prototypes For Autoencoder-based 3D Car Point Clouds
Thiago Rios;Jiawen Kong;Bas van Stein;Thomas Bäck;Patricia Wollstadt;Bernhard Sendhoff;Stefan Menzel
2020 IEEE Symposium Series on Computational Intelligence (SSCI)
Year: 2020 | Conference Paper |
Cited by: Papers (3)
Point cloud autoencoders were recently introduced as powerful models for data compression. They learn a lowdimensional set of variables that are suitable as design parameters for shape generation and optimization problems. In engineering tasks, 3D point clouds are often derived from fine polygon meshes, which are the most suitable representations for physics simulation, e.g., computational fluid d...Show More
Feature Visualization for 3D Point Cloud Autoencoders
Thiago Rios;Bas van Stein;Stefan Menzel;Thomas Back;Bernhard Sendhoff;Patricia Wollstadt
2020 International Joint Conference on Neural Networks (IJCNN)
Year: 2020 | Conference Paper |
Cited by: Papers (15)
In order to reduce the dimensionality of 3D point cloud representations, autoencoder architectures generate increasingly abstract, compressed features of the input data. Visualizing these features is central to understanding the learning process, however, while successful visualization techniques exist for neural networks applied to computer vision tasks, similar methods for geometric, especially ...Show More
On the Efficiency of a Point Cloud Autoencoder as a Geometric Representation for Shape Optimization
Thiago Rios;Bernhard Sendhoff;Stefan Menzel;Thomas Bäck;Bas van Stein
2019 IEEE Symposium Series on Computational Intelligence (SSCI)
Year: 2019 | Conference Paper |
Cited by: Papers (15)
A crucial step for optimizing a system is to formulate the objective function, and part of it concerns the selection of the design parameters. One of the major goals is to achieve a fair trade-off between exploring feasible solutions in the design space and maintaining admissible computational effort. In order to achieve such balance in optimization problems with Computer Aided Engineering (CAE) m...Show More
A New Approach Towards the Combined Algorithm Selection and Hyper-parameter Optimization Problem
2019 IEEE Symposium Series on Computational Intelligence (SSCI)
Year: 2019 | Conference Paper |
Cited by: Papers (5)
Machine learning algorithms often have many hyper-parameters that can be tuned to improve empirical performance. However, manually exploring the complex search spaces is tedious and cannot guarantee to find satisfactory outcomes. Recently, the Efficient Global Optimization (EGO) algorithm for solving the hyper-parameter optimization problem have shown substantial improvements. However, these algor...Show More
A multi-method simulation of a high-frequency bus line
Thierry van der Spek;Bas van Stein;Marcel van der Holst;Thomas Bäck
2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)
Year: 2017 | Conference Paper |
Cited by: Papers (2)
A methodology to model high-frequency bus lines is proposed and a realistic event-simulation model for such a line in the Netherlands is presented. This simulation model helps policy makers to predict changes that have to be made to bus routes and planned travel times before problems occur. With this model, different passenger growth scenarios can be easily evaluated. The model is validated using ...Show More
A new acquisition function for Bayesian optimization based on the moment-generating function
2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Year: 2017 | Conference Paper |
Cited by: Papers (34)
Bayesian Optimization or Efficient Global Optimization (EGO) is a global search strategy that is designed for expensive black-box functions. In this algorithm, a statistical model (usually the Gaussian process model) is constructed on some initial data samples. The global optimum is approached by iteratively maximizing a so-called acquisition function, that balances the exploration and exploitatio...Show More
Towards Data Driven Process Control in Manufacturing Car Body Parts
Bas Van Stein;Matthijs Van Leeuwen;Hao Wang;Stephan Purr;Sebastian Kreissl;Josef Meinhardt;Thomas Bäck
2016 International Conference on Computational Science and Computational Intelligence (CSCI)
Year: 2016 | Conference Paper |
Cited by: Papers (4)
The manufacturing process of car body parts is a complex industrial process where many machine parameters and material measurements are involved in establishing the quality of the final product. Data driven models have shown great advantages in helping decision makers to optimize this kind of complex processes where good physical models are hard to build. In this paper a framework for on-line proc...Show More
A framework for evaluating meta-models for simulation-based optimisation
2016 IEEE Symposium Series on Computational Intelligence (SSCI)
Year: 2016 | Conference Paper |
Cited by: Papers (1)
In order to evaluate complex and computationally expensive experiments, data-driven meta-models are used to replace costly experiments and approximate the real experiments' outcome. In this study, an evaluation framework is proposed for measuring the performance of these models. Explored is how the performance is related to the difference between the benchmark optimum and the model optimum, and a ...Show More
Local subspace-based outlier detection using global neighbourhoods
2016 IEEE International Conference on Big Data (Big Data)
Year: 2016 | Conference Paper |
Cited by: Papers (22)
Outlier detection in high-dimensional data is a challenging yet important task, as it has applications in, e.g., fraud detection and quality control. State-of-the-art density-based algorithms perform well because they 1) take the local neighbourhoods of data points into account and 2) consider feature subspaces. In highly complex and high-dimensional data, however, existing methods are likely to o...Show More
Fuzzy clustering for Optimally Weighted Cluster Kriging
Bas van Stein;Hao Wang;Wojtek Kowalczyk;Michael Emmerich;Thomas Bäck
2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Year: 2016 | Conference Paper |
Cited by: Papers (4)
Kriging or Gaussian Process Regression has been successfully applied in many fields. One of the major bottlenecks of Kriging is the complexity in both processing time (cubic) and memory (quadratic) in the number of data points. To overcome these limitations, a variety of approximation algorithms have been proposed. One of these approximation algorithms is Optimally Weighted Cluster Kriging (OWCK)....Show More
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