document: Genetic algorithm guided selection : variable selection and subset selection A novel genetic algorithm guided selection method , GAS , has been described . The method utilizes a simple encoding scheme which can represent both compounds and variables used to construct a QSAR/QSPR model . A genetic algorithm is then utilized to simultaneously optimize the encoded variables that include both descriptors and compound subsets . The GAS method generates multiple models each applying to a subset of the compounds . Typically the subsets represent clusters with different chemotypes . Also a procedure based on molecular similarity is presented to determine which model should be applied to a given test set compound . The variable selection method implemented in GAS has been tested and compared using the Selwood data set -LRB- n = 31 compounds ; nu = 53 descriptors -RRB- . The results showed that the method is comparable to other published methods . The subset selection method implemented in GAS has been first tested using an artificial data set -LRB- n = 100 points ; nu = 1 descriptor -RRB- to examine its ability to subset data points and second applied to analyze the XLOGP data set -LRB- n = 1831 compounds ; nu = 126 descriptors -RRB- . The method is able to correctly identify artificial data points belonging to various subsets . The analysis of the XLOGP data set shows that the subset selection method can be useful in improving a QSAR/QSPR model when the variable selection method fails
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