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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Villanova, L. Falcaro, P. Carta, D. Poli, I. Hyndman, R. Smith-Miles, K. |
| Copyright Year | 2010 |
| Description | Author affiliation: Associazione CIVEN - Nanofabrication Facility, Via delle Industrie 9, 30175 Marghera, Venezia, Italy (Carta, D.) || Department of Statistical Sciences, University of Padova, Via Cesare Battisti 241, 35121 Padova, Italy (Villanova, L.) || Department of Statistics, University Ca' Foscari of Venice, San Giobbe, Cannareggio 873, 30121 Venezia, Italy (Poli, I.) || School of Mathematical Sciences, Faculty of Science, Monash University, Building 28, Wellington Road, Clayton, Australia (Smith-Miles, K.) || CSIRO, Materials Science and Engineering, Gate 5 Normanby Rd, Clayton VIC 3168, Australia (Falcaro, P.) || Department of Econometrics and Business Statistics, Faculty of Business and Economics, Monash University, Building 11, Wellington Road, Clayton, Australia (Hyndman, R.) |
| Abstract | 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. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 696949 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424469093 |
| e-ISBN | 9781424469116 |
| DOI | 10.1109/CEC.2010.5586165 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-07-18 |
| Publisher Place | Spain |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Coatings Optimization Approximation methods Chemicals Glass Mars Computational modeling |
| Content Type | Text |
| Resource Type | Article |
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