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We propose and develop a new G-optimality criterion using the concept of weighted optimality criteria and certain additional generalizations. The goal of the weighted G-optimality is to minimize a weighted average of the maximum scaled prediction variance in the design region over a set of reduced models. A genetic algorithm (GA) is used for generating the weighted Goptimal exact designs in an experimental region for mixtures. The performance of the proposed GA designs is evaluated and compareddoi:10.5267/j.ijiec.2019.4.004 fatcat:tq7w7jlzlvfh5biqetks4sjs4u