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

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 multiobjective 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...

• Type: Conference
• Year: 2010
• Where: CEC
• Authors: Laura Villanova, Paolo Falcaro, Davide Carta, Irene Poli, Rob J. Hyndman, Kate Smith-Miles
• Keywords: Artificial Intelligence, CEC 2010, Dimensional Variable Space, Evolutionary Approach, Multivariate Adaptive Regression Splines