Mixture experiments involve developing a dedicated formulation for specific applications. We propose the weighted optimality criterion using the geometric mean as the objective function for the genetic algorithms. We generate a robust mixture design using genetic algorithms (GAs) of which the region of interest is an irregularly shaped polyhedral region formed by constraints on proportions of the mixture component. When specific terms in the initial model display unimportant effects, it is assumed that they are removed. The design generation objective requires model robustness across the set of the reduced models of the design. Proposing an alternative way to tackle the problem, we find that the proposed GA designs based on G- or/and IV-efficiency are robust to model misspecification.

中文翻译：

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使用几何平均值计算稳健的混合设计

混合物实验涉及为特定应用开发专用配方。我们提出了使用几何平均值作为遗传算法的目标函数的加权最优性准则。我们使用遗传算法 (GA) 生成稳健的混合设计，其中感兴趣的区域是由混合组件比例约束形成的不规则形状的多面体区域。当初始模型中的特定项显示不重要的影响时，假定它们已被删除。设计生成目标要求在设计的简化模型集上具有模型鲁棒性。提出了解决该问题的替代方法，我们发现基于 G 或/和 IV 效率的拟议 GA 设计对于模型指定错误具有鲁棒性。