Given an object of class mkm
and a set of tuning parameters,
max_EHVI performs the maximization of the Expected Hypervolume Improvement
criterion and delivers the next point to be visited in an HEGOlike
procedure.
1 2 
model 
An object of class 
lower 
Vector of lower bounds for the variables to be optimized over
(default: 0 with length 
upper 
Vector of upper bounds for the variables to be optimized over
(default: 1 with length 
control 
An optional list of control parameters, some of them passed to
the

optimcontrol 
Optional list of control parameters passed to the

A list with components:
par
The best set of parameters found.
value
The value of expected hypervolume improvement at par.
1 2 3 4 5 6 7 8 9  # 
# The Nowacki Beam
# 
n < 20
d < 2
doe < replicate(d,sample(0:n,n))/n
res < t(apply(doe, 1, nowacki_beam, box = data.frame(b = c(10, 50), h = c(50, 250))))
model < mkm(doe, res, modelcontrol = list(objective = 1:2, lower=c(0.1,0.1)))
max_EHVI(model)

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
Please suggest features or report bugs with the GitHub issue tracker.
All documentation is copyright its authors; we didn't write any of that.