Response surface methodology is widely used for developing, improving and optimizing processes in various fields. In this paper, we present a general algorithmic method for constructing 2q14q2 mixed-level designs in order to explore and optimize response surfaces with respect to D-efficiency, where the predictor variables are at two and four equally spaced levels, by utilizing a hybrid genetic algorithm. Emphasis is given on various properties that arise from the implementation of the genetic algorithm, such as using genetic operators as local optimizers and the representation of the four levels of the design with a 2-bit Gray Code. We applied the genetic algorithm in several cases and the optimized mixed-level designs achieve good properties, thus demonstrating the efficiency of the proposed hybrid heuristic.
Digital Object Identifier (DOI)
Angelopoulos, P.; Koukouvinos, C.; E. Simos, D.; and Skountzou, A.
"Mixed-Level Response Surface Designs via a Hybrid Genetic Algorithm,"
Journal of Statistics Applications & Probability: Vol. 2:
3, Article 5.
Available at: https://digitalcommons.aaru.edu.jo/jsap/vol2/iss3/5