no code implementations • 9 Sep 2022 • Alejandro Morales-Hernández, Inneke Van Nieuwenhuyse, Gonzalo Nápoles
The performance of any Machine Learning (ML) algorithm is impacted by the choice of its hyperparameters.
no code implementations • 5 Sep 2022 • Sebastian Rojas Gonzalez, Juergen Branke, Inneke Van Nieuwenhuyse
We consider bi-objective ranking and selection problems, where the goal is to correctly identify the Pareto optimal solutions among a finite set of candidates for which the two objective outcomes have been observed with uncertainty (e. g., after running a multiobjective stochastic simulation optimization procedure).
no code implementations • 16 Dec 2021 • Alejandro Morales-Hernández, Sebastian Rojas Gonzalez, Inneke Van Nieuwenhuyse, Ivo Couckuyt, Jeroen Jordens, Maarten Witters, Bart Van Doninck
Adhesive joints are increasingly used in industry for a wide variety of applications because of their favorable characteristics such as high strength-to-weight ratio, design flexibility, limited stress concentrations, planar force transfer, good damage tolerance, and fatigue resistance.
no code implementations • 9 Dec 2021 • Alejandro Morales-Hernández, Inneke Van Nieuwenhuyse, Sebastian Rojas Gonzalez, Jeroen Jordens, Maarten Witters, Bart Van Doninck
Automotive companies are increasingly looking for ways to make their products lighter, using novel materials and novel bonding processes to join these materials together.
no code implementations • 23 Nov 2021 • Alejandro Morales-Hernández, Inneke Van Nieuwenhuyse, Sebastian Rojas Gonzalez
Hyperparameter optimization (HPO) is a necessary step to ensure the best possible performance of Machine Learning (ML) algorithms.