Efficient computation of expected hypervolume improvement using box decomposition algorithms

In the field of multi-objective optimization algorithms, multi-objective Bayesian Global Optimization (MOBGO) is an important branch, in addition to evolutionary multi-objective optimization algorithms. MOBGO utilizes Gaussian Process models learned from previous objective function evaluations to de...

Full description

Saved in:

Bibliographic Details
Published in:	Journal of global optimization Vol. 75; no. 1; pp. 3 - 34
Main Authors:	Yang, Kaifeng, Emmerich, Michael, Deutz, André, Bäck, Thomas
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.09.2019 Springer Springer Nature B.V
Subjects:	Algorithms Analysis Bayesian analysis Computer Science Criteria Decomposition Evolutionary algorithms Gaussian process Global optimization Mathematical optimization Mathematics Mathematics and Statistics Multiple objective analysis Operations Research/Decision Theory Optimization Optimization algorithms Real Functions Upper bounds Expected hypervolume improvement Hypervolume indicator Probability of improvement Time complexity Kriging Multi-objective Bayesian global optimization
ISSN:	0925-5001, 1573-2916
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!