Functionalization of microarray devices: Process optimization using a multiobjective PSO and multiresponse MARS modeling

• Main Authors: Villanova, L, Falcaro, P, Carta, D, Poli, I, Hyndman, R, Smith-Miles, K
• Format: Conference Proceeding
• Language: English
• Subjects: Approximation methods; Chemicals; Coatings; Computational modeling; Glass; Mars; Optimization
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/CEC.2010.5586165

An evolutionary approach for the optimization of microarray coatings produced via sol-gel chemistry is presented. The aim of the methodology is to face the challenging aspects of the problem: unknown objective function, high dimensional variable space, constraints on the independent variables, multiple responses, expensive or time-consuming experimental trials, expected complexity of the functional relationships between independent and response variables. The proposed approach iteratively selects a set of experiments by combining a multiob-jective Particle Swarm Optimization (PSO) and a multiresponse Multivariate Adaptive Regression Splines (MARS) model. At each iteration of the algorithm the selected experiments are implemented and evaluated, and the system response is used as a feedback for the selection of the new trials. The performance of the approach is measured in terms of improvements with respect to the best coating obtained changing one variable at a time (the method typically used by scientists). Relevant enhancements have been detected, and the proposed evolutionary approach is shown to be a useful methodology for process optimization with great promise for industrial applications.