# GA

From LHD v0.1.0
0th

Percentile

##### Genetic Algorithm for LHD

GA returns a maximin distance LHD constructed by genetic algorithm (GA)

##### Usage
GA(n, k, m, N, pmut, p = 50, q = 1)
##### Arguments
n

A positive integer.

k

A positive integer.

m

A positive even integer.

N

A positive integer.

pmut

A probability.

p

A positive integer.

q

The default is set to be 1, and it could be either 1 or 2.

##### Details

• n stands for the number of rows (or run size).

• k stands for the number of columns (or the number of factors).

• m stands for the number of population and it must be an even number.

• N stands for the number of iterations.

• pmut stands for the probability of mutation.

• p is the parameter in the phi_p formula, and p is prefered to be large.

• If q is 1 (the default setting), dij is the rectangular distance. If q is 2, dij is the Euclidean distance.

##### Value

If all inputs are logical, then the output will be a n by k LHD.

##### References

Liefvendahl, M., and Stocki, R. (2006) A study on algorithms for optimization of Latin hypercubes. Journal of Statistical Planning and Inference, 136, 3231-3247.

• GA
##### Examples
# NOT RUN {
#create a 8 by 3 maximin distance LHD, with # of population and iterations = 10,
#the probability of mutation is 1/(k-1)
tryGA1=GA(n=8,k=3,m=10,N=10,pmut=1/(3-1),p=50,q=1)
tryGA1
phi_p(tryGA1,p=50)   #calculate the phi_p of "tryGA1".

#Another example with different n and k.
tryGA2=GA(n=12,k=2,m=10,N=10,pmut=1/(3-1),p=50,q=1)
tryGA2
phi_p(tryGA2,p=50)   #calculate the phi_p of "tryGA2".
# }

Documentation reproduced from package LHD, version 0.1.0, License: MIT + file LICENSE

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