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...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on cybernetics Vol. 50; no. 2; pp. 536 - 549
Main Authors:	Wang, Handing, Jin, Yaochu
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.02.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!