Surrogate-assisted bounding-box approach for optimization problems with tunable objectives fidelity
type: Item
instance of: scholarly article
title: Surrogate-assisted bounding-box approach for optimization problems with tunable objectives fidelity
zbMATH Open document ID: 1432.90097
author: Michel Rivier
author: P. M. Congedo
published in: Journal of Global Optimization
publication date: 2019-12-03T00:00:00Z
full work available at URL: RR-9155.pdf
Mathematics Subject Classification ID: 90C15
Mathematics Subject Classification ID: 90C29
zbMATH DE Number: 7139393
zbMATH Keywords: multi-objective optimization
zbMATH Keywords: uncertainty-based optimization
zbMATH Keywords: error bounding-boxes
zbMATH Keywords: tunable fidelity
zbMATH Keywords: surrogate-assisting strategy
MaRDI profile type: Publication
OpenAlex ID: W2970170808
cites work: Approximate implementations of pure random search in the presence of noise
cites work: An adaptive strategy on the error of the objective functions for uncertainty-based derivative-free optimization
cites work: Simulated annealing for noisy cost functions
cites work: Evolutionary Multi-Criterion Optimization
cites work: Comparing solutions under uncertainty in multiobjective optimization
cites work: Stratified Bayesian optimization
cites work: On similarities between two models of global optimization: Statistical models and radial basis functions
DOI: 10.1007/S10898-019-00823-9
description: scientific article; zbMATH DE number 7139393