LHD (version 1.1.0)

GA: Genetic Algorithm for LHD

Description

GA returns an LHD matrix generated by genetic algorithm (GA)

Usage

GA(n, k, m = 10, N = 10, pmut = 1/(k - 1), OC = "phi_p", p = 15, q = 1)

Arguments

n

A positive integer.

k

A positive integer.

m

A positive even integer.

N

A positive integer.

pmut

A probability.

OC

An optimality criterion.

p

A positive integer.

q

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

Value

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

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. The default is set to be 10.

• N stands for the number of iterations. The default is set to be 10.

• pmut stands for the probability of mutation. The default is set to be 1/(k - 1).

• OC stands for the optimality criterion, the default setting is "phi_p", and it could be one of the following: "phi_p", "AvgAbsCor", "MaxAbsCor", "MaxProCriterion".

• p is the parameter in the phi_p formula, and p is prefered to be large. The default is set to be 15.

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

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.

Examples

# NOT RUN {
#generate a 5 by 3 maximin distance LHD with the default setting
try=GA(n=5,k=3)
try
phi_p(try)   #calculate the phi_p of "try".

#Another example
#generate a 8 by 4 nearly orthogonal LHD
try2=GA(n=8,k=4,OC="AvgAbsCor")
try2
AvgAbsCor(try2)  #calculate the average absolute correlation.
# }