  Training population selection for genomic selection has captured a great deal
of interest in animal and plant breeding. In this article, we derive a
computationally efficient statistic to measure the reliability of estimates of
genetic breeding values for a fixed set of genotypes based on a given training
set of genotypes and phenotypes. We adopt a genetic algorithm scheme to find a
training set of certain size from a larger set of candidate genotypes that
optimizes this reliability measure. Our results show that, compared to a random
sample of the same size, phenotyping individuals selected by our method results
in models with better accuracies. We implement the proposed training selection
methodology on four data sets, namely, the arabidopsis, wheat, rice and the
maize data sets. Our results indicate that dynamic model building process which
uses genotypes of the individuals in the test sample into account while
selecting the training individuals improves the performance of GS models.
