Search Results for author: Ivo Couckuyt

Found 9 papers, 3 papers with code

Data-Efficient Interactive Multi-Objective Optimization Using ParEGO

no code implementations12 Jan 2024 Arash Heidari, Sebastian Rojas Gonzalez, Tom Dhaene, Ivo Couckuyt

Multi-objective optimization is a widely studied problem in diverse fields, such as engineering and finance, that seeks to identify a set of non-dominated solutions that provide optimal trade-offs among competing objectives.

Computational Efficiency Gaussian Processes

$\{\text{PF}\}^2$ES: Parallel Feasible Pareto Frontier Entropy Search for Multi-Objective Bayesian Optimization

1 code implementation11 Apr 2022 Jixiang Qing, Henry B. Moss, Tom Dhaene, Ivo Couckuyt

We present Parallel Feasible Pareto Frontier Entropy Search ($\{\text{PF}\}^2$ES) -- a novel information-theoretic acquisition function for multi-objective Bayesian optimization supporting unknown constraints and batch query.

Bayesian Optimization

Constrained multi-objective optimization of process design parameters in settings with scarce data: an application to adhesive bonding

no code implementations16 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.

Bayesian Optimization regression

GPflowOpt: A Bayesian Optimization Library using TensorFlow

1 code implementation10 Nov 2017 Nicolas Knudde, Joachim van der Herten, Tom Dhaene, Ivo Couckuyt

A novel Python framework for Bayesian optimization known as GPflowOpt is introduced.

Gaussian Processes

Active Learning for Approximation of Expensive Functions with Normal Distributed Output Uncertainty

no code implementations18 Aug 2016 Joachim van der Herten, Ivo Couckuyt, Dirk Deschrijver, Tom Dhaene

When approximating a black-box function, sampling with active learning focussing on regions with non-linear responses tends to improve accuracy.

Active Learning

Fast Calculation of the Knowledge Gradient for Optimization of Deterministic Engineering Simulations

no code implementations16 Aug 2016 Joachim van der Herten, Ivo Couckuyt, Dirk Deschrijver, Tom Dhaene

A novel efficient method for computing the Knowledge-Gradient policy for Continuous Parameters (KGCP) for deterministic optimization is derived.

Bayesian Optimization

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