# Latin Hypercube Sampling with a Genetic Algorithm

### Description

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.

### Usage

1 | ```
geneticLHS(n=10, k=2, pop=100, gen=4, pMut=.1, criterium="S", verbose=FALSE)
``` |

### Arguments

`n` |
The number of partitions (simulations or design points) |

`k` |
The number of replications (variables) |

`pop` |
The number of designs in the initial population |

`gen` |
The number of generations over which the algorithm is applied |

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

`criterium` |
The optimality criterium of the algorithm. Default is |

`verbose` |
Print informational messages. Default is |

### Details

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:

Generate

`pop`

random latin hypercube designs of size`n`

by`k`

Calculate the S optimality measure of each design

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

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

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

### Value

An `n`

by `k`

Latin Hypercube Sample matrix with values uniformly distributed on [0,1]

### Author(s)

Rob Carnell

### References

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
`augmentLHS`

to modify and augment existing designs.

### Examples

1 | ```
geneticLHS(4, 3, 50, 5, .25)
``` |