Max-Min-Distance Design
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.
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
# 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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