Thank you very much for your reply. I found the information on your home page very useful. What I want to do is a PLS regression of a data set with 60 features for calibration purposes. In order to optimize the performance of the calibration I have to find out what features to use in the PLS regression. Although this is what PLS-regression does but I found some articles (R. Leardi, "Genetic algorithms applied to feature selection in PLS regression: how and when to use them", Chemometrics and Intelligent Laboratory Systems 41 (1998) 195-207) that claim that a genetic optimization algorithm applied to feature selection could further improve the calibration. I found Python code that does exactly this but although I'm much more experienced in programming in Python I think it's probably better to use R for this task. So I'm going to implement the genetic algorithm described in the papers by Leardi in R. Regards Rolf
Patrick Burns wrote:
I don't know what you are trying to do exactly, but in case you don't find anything pre-built there is the 'Rgenoud' package for genetic optimization and also the 'genopt' function that you can get from S Poetry. Patrick Burns patrick at burns-stat.com +44 (0)20 8525 0696 http://www.burns-stat.com (home of S Poetry and "A Guide for the Unwilling S User") Rolf Wester wrote:
Hi, I'm looking for a R-package that does feature selection for PLS using a genetic optimization algorithm. I couldn't find one on CRAN and I wonder whether there is a free one. I would be very appreciative for any help. Regards Rolf
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------------------------------------ # Dr. Rolf Wester # Fraunhofer Institut f. Lasertechnik # Steinbachstrasse 15, D-52074 Aachen, Germany. # Tel: + 49 (0) 241 8906 401, Fax: +49 (0) 241 8906 121 # EMail: rolf.wester at ilt.fraunhofer.de # WWW: http://www.ilt.fraunhofer.de