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

Laura Villanova, Paolo Falcaro, Davide Carta, Irene Poli, Rob J Hyndman, Kate Smith-Miles
(2010) 2010 IEEE Congress on Evolutionary Computation, July 18-23, Barcelona, Spain

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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: 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 select a set of experiments by combining a multiobjective Particle Swarm Optimization (PSO) and a multiresponse Multivariate Adaptive Regression Spines (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 best coating identified using the described methodology is characterized by relevant improvements with respect to the best coating obtained changing one variable at a time. The proposed evolutionary approach is shown to be a useful methodology for process optimization with great promise for industrial applications.