Given an object of class mkm
and a set of tuning parameters,
max_EI performs the maximization of the Constrained Expected Improvement
criterion and delivers the next point to be visited in an MEGOlike
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.
Vector. The best set of parameters found.
1 2 3 4 5 6 7 8 9 10 11 12  # 
# BraninHoo function (with simple constraint)
# 
n < 10
d < 2
doe < replicate(d,sample(0:n,n))/n
fun_cost < DiceKriging::branin
fun_cntr < function(x) 0.2  prod(x)
fun < function(x) return(cbind(fun_cost(x),fun_cntr(x)))
res < t(apply(doe, 1, fun))
model < mkm(doe, res, modelcontrol = list(objective = 1, lower=c(0.1,0.1)))
max_EI(model)

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