# geneticLHS

From lhs v1.0.1
0th

Percentile

##### Latin Hypercube Sampling with a Genetic Algorithm

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.

Keywords
design
##### Usage
geneticLHS(n = 10, k = 2, pop = 100, gen = 4, pMut = 0.1,
criterium = "S", verbose = FALSE)
##### Arguments
n

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

k

The number of replications (variables or columns)

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 S. Maximin is also supported

verbose

Print informational messages. Default is FALSE

##### 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:

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

##### Value

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

##### 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 [augtmentLHS()] to modify and augment existing designs.

• geneticLHS
##### Examples
# NOT RUN {
set.seed(1234)
A <- geneticLHS(4, 3, 50, 5, .25)
# }

Documentation reproduced from package lhs, version 1.0.1, License: GPL-3

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