109 citations · 207 across the 23 of their papers we have counts for
26 papers
The Modular CMA-ES: A Framework for Modern Evolution Strategies
Jacob de Nobel, Diederick Vermetten, Anna V. Kononova +2
Since their introduction, modern evolution strategies, such as the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), have become established as powerful methods for continu…
DistributedDesignOptimizer: A modular Python framework for Setup, Execution and Processing of Distributed Design Optimization
Sebastian Ellmaier, Albert J. de Wit, Akilesh Raveendran +3
Multidisciplinary Design Optimization (MDO) enables the application of optimization algorithms to complex engineered systems, ranging from aerospace and automotive to robotics and…
Adversarial Latent Autoencoder with Self-Attention for Structural Image Synthesis
Jiajie Fan, Laure Vuaille, Hao Wang +1
Generative Engineering Design approaches driven by Deep Generative Models (DGM) have been proposed to facilitate industrial engineering processes. In such processes, designs often…
Representation-agnostic distance-driven perturbation for optimizing ill-conditioned problems
Kirill Antonov, Anna V. Kononova, Thomas Bäck +1
Locality is a crucial property for efficiently optimising black-box problems with randomized search heuristics. However, in practical applications, it is not likely to always find…
Challenges of ELA-guided Function Evolution using Genetic Programming
Fu Xing Long, Diederick Vermetten, Anna V. Kononova +4
Within the optimization community, the question of how to generate new optimization problems has been gaining traction in recent years. Within topics such as instance space analysi…
When to be Discrete: Analyzing Algorithm Performance on Discretized Continuous Problems
André Thomaser, Jacob de Nobel, Diederick Vermetten +3
The domain of an optimization problem is seen as one of its most important characteristics. In particular, the distinction between continuous and discrete optimization is rather im…
Application of quantum-inspired generative models to small molecular datasets
C. Moussa, H. Wang, M. Araya-Polo +2
Quantum and quantum-inspired machine learning has emerged as a promising and challenging research field due to the increased popularity of quantum computing, especially with near-t…
Modular Differential Evolution
Diederick Vermetten, Fabio Caraffini, Anna V. Kononova +1
New contributions in the field of iterative optimisation heuristics are often made in an iterative manner. Novel algorithmic ideas are not proposed in isolation, but usually as an…
Towards Self-adaptive Mutation in Evolutionary Multi-Objective Algorithms
Furong Ye, Frank Neumann, Jacob de Nobel +2
Parameter control has succeeded in accelerating the convergence process of evolutionary algorithms. While empirical and theoretical studies have shed light on the behavior of algor…
Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler
Frank Neumann, Aneta Neumann, Chao Qian +7
Submodular functions play a key role in the area of optimization as they allow to model many real-world problems that face diminishing returns. Evolutionary algorithms have been sh…
Multi-surrogate Assisted Efficient Global Optimization for Discrete Problems
Qi Huang, Roy de Winter, Bas van Stein +2
Decades of progress in simulation-based surrogate-assisted optimization and unprecedented growth in computational power have enabled researchers and practitioners to optimize previ…
Optimizing Stimulus Energy for Cochlear Implants with a Machine Learning Model of the Auditory Nerve
Jacob de Nobel, Anna V. Kononova, Jeroen Briaire +2
Performing simulations with a realistic biophysical auditory nerve fiber model can be very time consuming, due to the complex nature of the calculations involved. Here, a surrogate…
Optimally Weighted Ensembles of Regression Models: Exact Weight Optimization and Applications
Patrick Echtenbruck, Martina Echtenbruck, Joost Batenburg +3
Automated model selection is often proposed to users to choose which machine learning model (or method) to apply to a given regression task. In this paper, we show that combining d…
Hyperparameter Importance of Quantum Neural Networks Across Small Datasets
Charles Moussa, Jan N. van Rijn, Thomas Bäck +1
As restricted quantum computers are slowly becoming a reality, the search for meaningful first applications intensifies. In this domain, one of the more investigated approaches is…
Analyzing the Impact of Undersampling on the Benchmarking and Configuration of Evolutionary Algorithms
Diederick Vermetten, Hao Wang, Manuel López-Ibañez +2
The stochastic nature of iterative optimization heuristics leads to inherently noisy performance measurements. Since these measurements are often gathered once and then used repeat…
Switching between Numerical Black-box Optimization Algorithms with Warm-starting Policies
Dominik Schröder, Diederick Vermetten, Hao Wang +2
When solving optimization problems with black-box approaches, the algorithms gather valuable information about the problem instance during the optimization process. This informatio…
Unsupervised strategies for identifying optimal parameters in Quantum Approximate Optimization Algorithm
Charles Moussa, Hao Wang, Thomas Bäck +1
As combinatorial optimization is one of the main quantum computing applications, many methods based on parameterized quantum circuits are being developed. In general, a set of para…
Performance comparison of optimization methods on variational quantum algorithms
Xavier Bonet-Monroig, Hao Wang, Diederick Vermetten +5
Variational quantum algorithms (VQAs) offer a promising path toward using near-term quantum hardware for applications in academic and industrial research. These algorithms aim to f…
Deep Multiagent Reinforcement Learning: Challenges and Directions
Annie Wong, Thomas Bäck, Anna V. Kononova +1
This paper surveys the field of deep multiagent reinforcement learning. The combination of deep neural networks with reinforcement learning has gained increased traction in recent…
Is there Anisotropy in Structural Bias?
Diederick Vermetten, Anna V. Kononova, Fabio Caraffini +2
Structural Bias (SB) is an important type of algorithmic deficiency within iterative optimisation heuristics. However, methods for detecting structural bias have not yet fully matu…
Finding Efficient Trade-offs in Multi-Fidelity Response Surface Modeling
Sander van Rijn, Sebastian Schmitt, Matthijs van Leeuwen +1
In the context of optimization approaches to engineering applications, time-consuming simulations are often utilized which can be configured to deliver solutions for various levels…
Learning adaptive differential evolution algorithm from optimization experiences by policy gradient
Jianyong Sun, Xin Liu, Thomas Bäck +1
Differential evolution is one of the most prestigious population-based stochastic optimization algorithm for black-box problems. The performance of a differential evolution algorit…
Tabu-driven Quantum Neighborhood Samplers
Charles Moussa, Hao Wang, Henri Calandra +2
Combinatorial optimization is an important application targeted by quantum computing. However, near-term hardware constraints make quantum algorithms unlikely to be competitive whe…
Neural Network Design: Learning from Neural Architecture Search
Bas van Stein, Hao Wang, Thomas Bäck
Neural Architecture Search (NAS) aims to optimize deep neural networks' architecture for better accuracy or smaller computational cost and has recently gained more research interes…