optimMaxMinDist {CEGO}

Max-Min-Distance Optimizer

Description

One-shot optimizer: Create a design with maximum sum of distances, and evaluate. Best candidate is returned.

Usage

optimMaxMinDist(x = NULL, fun, control = list())

Arguments

x

Optional set of solution(s) as a list, which are added to the randomly generated solutions and are also evaluated with the target function.

fun

target function to be minimized

control

(list), with the options:

budget

The limit on number of target function evaluations (stopping criterion) (default: 100).

vectorized

Boolean. Defines whether target function is vectorized (takes a list of solutions as argument) or not (takes single solution as argument). Default: FALSE.

creationFunction

Function to create individuals/solutions in search space. Default is a function that creates random permutations of length 6.

designBudget

budget of the design function designMaxMinDist, which is the number of randomly created candidates in each iteration.

Value

a list:

xbest

best solution found

ybest

fitness of the best solution

x

history of all evaluated solutions

y

corresponding target function values f(x)

count

number of performed target function evaluations

See Also

optimCEGO, optimEA, optimRS, optim2Opt

Examples

seed=0
#distance
dF <- distancePermutationHamming
#creation
cF <- function()sample(5)
#objective function
lF <- landscapeGeneratorUNI(1:5,dF)
#start optimization
set.seed(seed)
res <- optimMaxMinDist(,lF,list(creationFunction=cF,budget=20,
	vectorized=TRUE)) ##target function is "vectorized", expects list as input
res$xbest