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General Information
Prof. Wael Badawy
Department of Computing and Information Systems Umm Al Qura University, Canada
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.
IJCTE 2015 Vol.7(5): 394-397 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2015.V7.992

Computation of Parameters Using Genetic Algorithm and Sequential Quadratic Programming in Sampling

Nursel Koyuncu
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).


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.

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