# designMaxMinDist: Max-Min-Distance Design In CEGO: Combinatorial Efficient Global Optimization

## Description

Build a design of experiments in a sequential manner: First candidate solution is created at random. Afterwards, candidates are added sequentially, maximizing the minimum distances to the existing candidates. Each max-min problem is resolved by random sampling. The aim is to get a rather diverse design.

## Usage

 `1` ```designMaxMinDist(x = NULL, cf, size, control = list()) ```

## Arguments

 `x` Optional list of user specified solutions to be added to the design/population, defaults to NULL `cf` Creation function, creates random new individuals `size` size of the design `control` list of controls. `control\$distanceFunction` requires a distance function to compare two candidates created by cf. `control\$budget` is the number of candidates for the random sampling, defaults to 100.

## Value

Returns list with experimental design without duplicates

## See Also

`optimMaxMinDist`, `designRandom`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```# Create a design of 10 permutations, each with n=5 elements, # and with 50 candidates for each sample. # Note, that in this specific case the number of candidates # should be no larger than factorial(n). # The default (hamming distance) is used. design <- designMaxMinDist(NULL,function()sample(5),10, control=list(budget=50)) # Create a design of 20 real valued 2d vectors, # with 100 candidates for each sample # using euclidean distance. design <- designMaxMinDist(NULL,function()runif(2),20, control=list(budget=100, distanceFunction=function(x,y)sqrt(sum((x-y)^2)))) # plot the resulting design plot(matrix(unlist(design),,2,byrow=TRUE)) ```

CEGO documentation built on May 14, 2021, 1:08 a.m.