Using a Genetic Algorithm to Generate D-optimal Designs for Mixture Experiments

We propose and develop a genetic algorithm (GA) for generating D-optimal designs where the experimental region is an irregularly shaped polyhedral region. Our approach does not require selection of points from a user-defined candidate set of mixtures and allows movement through a continuous region that includes highly constrained mixture regions. This approach is useful in situations where extreme vertices (EV) designs or conventional exchange algorithms fail to find a near-optimal design. For illustration, examples with three and four components are presented with comparisons of our GA designs with those obtained using EV designs and exchange-point algorithms over an irregularly shaped polyhedral region. The results show that the designs produced by the GA perform better than, if not as well as, the designs produced by the exchange-point algorithms; however, the designs produced by the GA perform better than the designs produced by the EV. This suggests that GA is an alternative approach for constructing the D-optimal designs in problems of mixture experiments when EV designs or exchange-point algorithms are insufficient. Copyright © 2012 John Wiley & Sons, Ltd.