A Random Forest-Assisted Evolutionary Algorithm for Data-Driven Constrained Multiobjective Combinatorial Optimization of Trauma Systems

Many real-world optimization problems can be solved by using the data-driven approach only, simply because no analytic objective functions are available for evaluating candidate solutions. In this paper, we address a class of expensive data-driven constrained multiobjective combinatorial optimizatio...

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Vydáno v:	IEEE transactions on cybernetics Ročník 50; číslo 2; s. 536 - 549
Hlavní autoři:	Wang, Handing, Jin, Yaochu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.02.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Combinatorial analysis Computational modeling Constrained multiobjective combinatorial optimization Constraint modelling data-driven optimization evolutionary algorithm (EA) Evolutionary algorithms Forestry Linear programming Mathematical programming Multiple objective analysis Optimization Radial basis function radial basis function (RBF) networks Radio frequency random forest (RF) Regression analysis Regression models surrogate Systems design Training trauma systems Vegetation
ISSN:	2168-2267, 2168-2275, 2168-2275
On-line přístup:	Získat plný text
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