geneticLHS: Latin Hypercube Sampling with a Genetic Algorithm

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/geneticLHS.R


Draws a Latin Hypercube Sample from a set of uniform distributions for use in creating a Latin Hypercube Design. This function attempts to optimize the sample with respect to the S optimality criterion through a genetic type algorithm.


  n = 10,
  k = 2,
  pop = 100,
  gen = 4,
  pMut = 0.1,
  criterium = "S",
  verbose = FALSE



The number of partitions (simulations or design points or rows)


The number of replications (variables or columns)


The number of designs in the initial population


The number of generations over which the algorithm is applied


The probability with which a mutation occurs in a column of the progeny


The optimality criterium of the algorithm. Default is S. Maximin is also supported


Print informational messages. Default is FALSE


Latin hypercube sampling (LHS) was developed to generate a distribution of collections of parameter values from a multidimensional distribution. A square grid containing possible sample points is a Latin square iff there is only one sample in each row and each column. A Latin hypercube is the generalisation of this concept to an arbitrary number of dimensions. When sampling a function of k variables, the range of each variable is divided into n equally probable intervals. n sample points are then drawn such that a Latin Hypercube is created. Latin Hypercube sampling generates more efficient estimates of desired parameters than simple Monte Carlo sampling.

This program generates a Latin Hypercube Sample by creating random permutations of the first n integers in each of k columns and then transforming those integers into n sections of a standard uniform distribution. Random values are then sampled from within each of the n sections. Once the sample is generated, the uniform sample from a column can be transformed to any distribution by using the quantile functions, e.g. qnorm(). Different columns can have different distributions.

S-optimality seeks to maximize the mean distance from each design point to all the other points in the design, so the points are as spread out as possible.

Genetic Algorithm:

  1. Generate pop random latin hypercube designs of size n by k

  2. Calculate the S optimality measure of each design

  3. Keep the best design in the first position and throw away half of the rest of the population

  4. Take a random column out of the best matrix and place it in a random column of each of the other matricies, and take a random column out of each of the other matricies and put it in copies of the best matrix thereby causing the progeny

  5. For each of the progeny, cause a genetic mutation pMut percent of the time. The mutation is accomplished by swtching two elements in a column


An n by k Latin Hypercube Sample matrix with values uniformly distributed on [0,1]


Rob Carnell


Stocki, R. (2005) A method to improve design reliability using optimal Latin hypercube sampling Computer Assisted Mechanics and Engineering Sciences 12, 87–105.

Stein, M. (1987) Large Sample Properties of Simulations Using Latin Hypercube Sampling. Technometrics. 29, 143–151.

See Also

[randomLHS()], [improvedLHS()], [maximinLHS()], and [optimumLHS()] to generate Latin Hypercube Samples. [optAugmentLHS()] [optSeededLHS()], and [augtmentLHS()] to modify and augment existing designs.


A <- geneticLHS(4, 3, 50, 5, .25)

Example output

lhs documentation built on Oct. 23, 2020, 7:17 p.m.