Supersaturated experimental designs: new approaches to building and using it: Part II. Solving supersaturated designs by genetic algorithms

A new procedure for the resolution of supersaturated matrices is presented. This procedure is based on genetic algorithm-driven regression, using an efficient system of genetic operatorsʹ competence. Genetic regression is compared with stepwise regression and all-subsets regression that has been proposed to solve this type of matrices. Presented results show that this new genetic-driven procedure outperforms both stepwise and all-subsets regression procedures. The proposed method shows satisfactory efficiency and robustness for data even in the presence of gross accidental errors. All studies were conducted by a dedicated toolkit that is briefly described.