Abstract—The sampling literature contains many examples
of estimators of population parameter. To deal with this
problem many authors have suggested family of estimators of
population parameter. But in the case of generalization of these
estimators, estimation of optimum values is a problem. Some
authors can define estimator replacing the unknown
parameters by their sample estimates. To get the optimum
estimator, one need to solve complex mean square error
equation with many parameters and nonlinear constraints. In
this study we have tried to get these optimum parameter in
stratified random sampling using genetic algorithms and
sequential quadratic programming. A numerical example is
also done to compare these algorithms. The results show that
genetic algorithm is more efficient than sequential quadratic
programming to solve the complex model with more
parameters under non-linear constraints.
Index Terms—Efficiency, genetic algorithm, mean square
error, stratified random sampling.
The author is with the Department of Statistics, Hacettepe University,
Beytepe, Ankara, Turkey (e-mail: author@lamar.colostate.edu).
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Cite:Computation of Parameters Using Genetic Algorithm and Sequential Quadratic Programming in Sampling, "Nursel Koyuncu," International Journal of Computer Theory and Engineering vol. 7, no. 5, pp. 394-397, 2015.
