Local function approximation in evolutionary algorithms for the optimization of costly functions

We develop an approach for the optimization of continuous costly functions that uses a space-filling experimental design and local function approximation to reduce the number of function evaluations in an evolutionary algorithm. Our approach is to estimate the objective function value of an offsprin...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 8; H. 5; S. 490 - 505
Hauptverfasser:	Regis, R.G., Shoemaker, C.A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.10.2004 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Analysis of variance Applied sciences Approximation Approximation algorithms Artificial intelligence Computer science; control theory; systems Design engineering Design for experiments Design optimization Evolutionary algorithms Evolutionary computation Exact sciences and technology Finite element methods Function approximation Hypercubes Interpolation Mathematical analysis Mathematical models Mathematical programming Operational research and scientific management Operational research. Management science Optimization Problem solving, game playing Testing Statistical analysis Evolutionary algorithm evolutionary algorithm (EA) Numerical method Nonlinear problems Symmetric function Approximation algorithm quadratic regression Variance analysis Modeling Optimization radial basis function (RBF) Radial basis function Finite element method Algorithm performance Genetic algorithm function approximation Approximation by function Quadratic approximation Cost function Latin hypercube Objective function Costly function
ISSN:	1089-778X, 1941-0026
Online-Zugang:	Volltext
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