Evolutionary Programming for High-Dimensional Constrained Expensive Black-Box Optimization Using Radial Basis Functions

This paper develops a surrogate-assisted evolutionary programming (EP) algorithm for constrained expensive black-box optimization that can be used for high-dimensional problems with many black-box inequality constraints. The proposed method does not use a penalty function and it builds surrogates fo...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 18; H. 3; S. 326 - 347
1. Verfasser:	Regis, Rommel G.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.06.2014 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptation models Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Black-box optimization Computational modeling Computer science; control theory; systems Constrained Optimization Constraints Construction Evolutionary algorithms Evolutionary computation Evolutionary programming Exact sciences and technology Genetic algorithms Highdimensional optimization Mathematical analysis Mathematical models Optimization Parents Programming Radial basis function Radial basis functions Simulation Software Surrogateassisted evolutionary algorithms Theoretical computing surrogate-assisted evolutionary algorithms high-dimensional optimization evolutionary programming radial basis functions constrained optimization Black-box optimization Motor car Search strategy Probabilistic approach Hierarchical classification Decision making Evolutionary algorithm Black box Constrained optimization Radial basis function Penalty function Assisted programming Value function Multidimensional analysis Inequality constraint Cubics Objective function Metamodel
ISSN:	1089-778X, 1941-0026
Online-Zugang:	Volltext
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