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.
Laura Villanova, Kate Smith-Miles and Rob J Hyndman
Maintainer: Laura Villanova <[email protected]>
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14
in.name <- c("x1","x2") nlev <- c(20, 20) lower <- c(-3, -3) upper <- c(3, 3) out.name <- "y" weight <- 1 C <- 20 pr.mut <- c(0.1, 0.07, 0.04, rep(0.01, C-3)) emma.peaks<-emma(in.name, nlev, lower, upper, out.name, opt = "mx", nd = 10, na = 5, weight, C , w1 = 0.7, w2 = 0.4, c1i = 2.5, c1f = 0.5, c2i = 0.5, c2f = 2.5, b = 5, pr.mut, graph = "no", fn1 = peaks) plot(emma.peaks, fn = peaks, n = 50, C = 20)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.