The evolutionary model-based multiresponse approach (EMMA) is a procedure for process optimization
and product improvement. It is particularly suited to processes featuring irregular experimental
region due to constraints on the input variables (factors), multiple responses not accomodated
by polynomial models, and expensive or time-consuming experiments. EMMA iterativelly selects
new experimental points that increasingly concentrate on the most promising regions of the
experimental space. The selection of the new experimental points is performed on the basis of
the results achieved during previous trials. A multivariate adaptive regression splines (MARS)
model and a particle swarm optimization (PSO) algorithm are used to drive the search of the
optimum.
Details
Package:
emma
Type:
Package
Version:
1.0
Date:
2011-02-22
License:
GPL (>=2)
LazyLoad:
yes
Author(s)
Laura Villanova, Kate Smith-Miles and Rob J Hyndman
Maintainer: Laura Villanova <laura.villanova@monash.edu>
References
Villanova L., Falcaro P., Carta D., Poli I., Hyndman R., Smith-Miles K. (2010)
'Functionalization of Microarray Devices: Process Optimization Using a
Multiobjective PSO and Multiresponse MARS Modelling', IEEE CEC 2010,
DOI: 10.1109/CEC.2010.5586165
Carta D., Villanova L., Costacurta S., Patelli A., Poli I., Vezzu' S.,
Scopece P., Lisi F., Smith-Miles K., Hyndman R. J., Hill A. J.,
Falcaro P. (2011) 'Method for Optimizing Coating Properties Based
on an Evolutionary Algorithm Approach', Analytical Chemistry 83
(16), 6373-6380.