The 2k−p fractional factorial design is the most widely used technique for industrial experimentation. This is because it can significantly reduce the number of experimental runs so that the application of experimental design to problems with a large number of factors becomes possible. However, the application of this technique usually causes the loss of important information. That is, some effects of the experiment may confound with each other and cannot be clearly identified. The follow-up design is a tool used to untangle the confounded effects produced in the initial experiment. In this research, a heuristic based on an effective evolutionary algorithm, Genetic Algorithms, has been developed to generate the optimal follow-up design. The heuristic has been applied in two common test examples. The result showed that the heuristic could simply find optimal follow-up designs, and dominate the existing algorithm.
