Genetic algorithm subset selection

Another application of GAs was published by Aires de Sousa et al. they used genetic algorithms to select the appropriate descriptors for representing structure-chemical shift correlations in the computer [69]. Each chromosome was represented by a subset of 486 potentially useful descriptors for predicting H-NMR chemical shifts. The task of a fitness function was performed by a CPG neural network that used the subset of descriptors encoded in the chromosome for predicting chemical shifts. Each proton of a compound is presented to the neural network as a set of descriptors obtaining a chemical shift as output. The fitness function was the RMS error for the chemical shifts obtained from the neural network and was verified with a cross-validation data set. 

S.J. Cho, M.A. Hermsmeier, Genetic Algorithm Guided Selection Variable Selection and Subset Selection, J. Chem. Inf. Comput. Sd. 42 (2002) 927-936. 

Models developed with selected subsets of descriptors, instead of all possible descriptors, can be more accurate and robust. In order to select adequate descriptors for each of the four classes of protons, genetic algorithms (GA) were used, and the results were compared with those obtained when all the descriptors were used. 

As might be expected, established optimisation techniques such as simulated annealing and genetic algorithms have been used to tackle the subset selection problem. These methods... 

We may suppose that not all 600 wavelengths are useful for the prediction of nitrogen contents. A variable selection method called genetic algorithm (GA, Section 4.5.6) has been applied resulting in a subset with only five variables (wavelengths). Figure 1.3c and d shows that models with these five variables are better than models... 

Selecting the right variables often improves the models and makes interpretation easier. When there are too many descriptors, and especially when these descriptors do not have a clear physico-chemical meaning (e.g., connectivity indices and other 2D descriptors), stochastic methods such as genetic algorithms and evolutionary strategies can be used for finding an optimal subset of descriptors [91,92]. 

Variable selection is performed by using Genetic Algorithms (GA), based on the evolution of a population of models. In genetic algorithm terminology, the binary vector I is called a chromosome, which is a p-dimensional vector where each position (a gene) corresponds to a variable (1 if included in the model, 0 otherwise). Each chromosome represents a model with a subset of variables. 

Generating Optimal Linear PLS Estimations = GOLPE variable selection > genetic algorithm - variable subset selection variable selection... 

This procedure can be used to perform a preliminary screening of the variables, while other techniques are used on the remaining variables, that is, genetic algorithm variable subset selection or searching for all subset models. A disadvantage of this procedure is that a variable with a low correlation with the response, but correlated with residuals and thus able to give a contribution in the final model, is excluded. 

Pavan, M., Consonni, V. and Todeschini, R. (2005) Partial ranking models by genetic algorithms variable subset selection (GA-VSS) approach for environmental priority settings. MATCH Commun. Math. Comput. Chem., 54, 583-609. 

Todeschini, R., Consonni, V., Mauri, A. and Pavan, M. (2004b) New fitness frmctions to avoid bad regression models in variable subset selection by genetic algorithms, in Designing Drugs and Crop Protectants Processes, Problems and Solutions (eds M. Ford, D.J. Livingstone, J.C. Dearden and H. van de Waterbeemd), Blackwell, Oxford, UK, pp. 323-325. 

New QSAR Modelling Approach Based on Ranking Models by Genetic Algorithms - Variable Subset Selection (GA-VSS)... 

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