Keep the best design in the first position and throw away half of the rest of the population Generate pop random latin hypercube designs of size n by kĬalculate the S optimality measure of each design The other points in the design, so the points are as spread out as possible. S-optimality seeks to maximize the mean distance from each design point to all The uniform sample from a column can be transformed to any distribution by Then sampled from within each of the n sections. Integers into n sections of a standard uniform distribution. Of the first n integers in each of k columns and then transforming those This program generates a Latin Hypercube Sample by creating random permutations Latin Hypercube sampling generates more efficientĮstimates of desired parameters than simple Monte Carlo sampling. n sample points are then drawn such that a Sampling a function of k variables, the range of each variable is divided Generalisation of this concept to an arbitrary number of dimensions. Is only one sample in each row and each column. Of collections of parameter values from a multidimensional distribution.Ī square grid containing possible sample points is a Latin square iff there Latin hypercube sampling (LHS) was developed to generate a distribution Default is FALSEĪn n by k Latin Hypercube Sample matrix with values uniformly distributed on Details The optimality criterium of the algorithm. The probability with which a mutation occurs in a column of the progeny The number of generations over which the algorithm is applied The number of designs in the initial population The number of replications (variables or columns) The number of partitions (simulations or design points or rows) Sample with respect to the S optimality criterion through a genetic type Draws a Latin Hypercube Sample from a set of uniform distributions for use inĬreating a Latin Hypercube Design.