CEGO (version 2.4.3)

optimMaxMinDist: 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())

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

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.

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