A committee machine approach to multiple response optimization

Multiple responses optimization problems have three phases including design of experiments, modeling and optimization. Artificial neural networks and genetic algorithm are applied for second and third phases. Committee machines include some experts such as some neural networks which operate together to get response. Current article applies a committee machine including four different artificial neural networks to model multiple responses optimization problems. Genetic algorithm is applied to calculate weights of committee machine and also it optimizes desirability function of all responses to get optimum point. Seven different cases in multiple responses optimization were modeled and analyzed. The results show the error of committee machine is near half of average error of artificial neural networks and global desirability of committee machine is the same as average global desirability of artificial neural networks.

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