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EI

Calculates (one point) Expected improvement.

Calling Sequence

Ei=EI(opt,x)

Parameters

opt:

mlist of type EGO.

x:

matrix (mx,nx) of points at which calculates (one point) Ei, mx rows for mx data points, nx columns for each dimension.

Ei:

expected improvement at points x.

Description

Calculates (one point) Expected improvement.

Examples

// 1D example
function [y]=gpeak(x)

y=sin(x*3)-exp(-(x+0.1)^2/0.01)
endfunction;

ftest=gpeak;
x_bound=[-1; 1];

noise=0;

X=(linspace(x_bound(1),x_bound(2),6))';// six initial points
YExp=ftest(X); // response

Xtest=(linspace(x_bound(1),x_bound(2),100))'; // test points

// creaate initial kriging model
[kmodel] =km(X,YExp,0,'gaussian',1,0.0000001);
kmodel.estimateNoise=0;

[kmodel p]=findTheta(kmodel,0,5,'MLL','RS',100);

// ego boundaries
lob=[-1];
upb=[1];
maxit=16;

opt=EGO(kmodel,lob,upb,maxit,0.00000001,'RS');

x=RLHS(10,1,lob,upb);
EI(opt,x)

scf();
plot(Xtest,EI(opt,Xtest));

See also

Authors

Bibliography

Jones D.R., Schonlau M. and Welch W.J. (1998), Efficient Global Optimization of Expensive Black-Box Functions, Journal of Global optimization, 13:, 455-492.

//

pause;

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