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...

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Vydáno v:	Journal of global optimization Ročník 75; číslo 1; s. 3 - 34
Hlavní autoři:	Yang, Kaifeng, Emmerich, Michael, Deutz, André, Bäck, Thomas
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.09.2019 Springer Springer Nature B.V
Témata:	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
On-line přístup:	Získat plný text
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