Description Usage Arguments Value See Also Examples

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.

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

`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. |

Returns list with experimental design without duplicates

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))
``` |

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