<![CDATA[In certain fields, experiments involve many factors and are constrained by costs. Reducing runs is one of the solutions to reduce experiment costs. But that can cause the number of runs to become less than the number of factors. This case of experimental design also is known as a supersaturated design. The important factors in this design are generally estimated by involving variable selection such as forward selection, stepwise regression, and penalized regression. Genetic algorithm is one of the methods that can be used for variable selection, especially for high dimensional data or supersaturated design. This study aims to use a genetic algorithm for variable selection in the supersaturated design and compare the genetic algorithm results with a stepwise regression which is generally used for a simple design. This study also involved fractional factorial design principles. The result showed that the main factors and interactions of the genetic algorithm and stepwise regression were quite different. But the principle was the same because the variables correlated. The genetic algorithm model had a smaller AIC and BIC and all of the main factors and interactions which had chosen were significant on the 0.1%. Therefore genetic algorithm model was chosen although computation time was much longer than stepwise regression.]]>
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