Design of robust parameter experiments in a continuous space using an evolutionary optimization algorithm

Two common experimental designs used in robust parameter design (RPD) are crossed array and mixed resolution designs. However, the prohibited number of runs, constraints in the design space or special model requirements render some of these designs inadequate. This paper presents the application of an evolutionary strategy to produce nearly optimal design matrices for RPD. The designs are derived by solving a nonlinear optimization problem involving both 𝒟- and 𝒢-efficiency simultaneously. The methodology presented allows the user to obtain new exact designs for a specific number of runs, and a particular experimental region. The combination of 𝒟- and 𝒢-efficiency results in experimental designs that outperform the corresponding benchmarks.