Sentence examples for generated tested with from inspiring English sources

GARP is genetic, meaning that rule development is done through an automated process, whereby rules are randomly generated, tested with internal statistical tests, and modified (through the rules of genetic evolution point mutations, crossovers, deletions, insertions) [69] to determine which rules to keep and delete based on their accuracy at predicting internal testing data.

It also allows them to generate tests with any level of complexity they like even ones that are far beyond the ability of humans to complete.

For the original experiment, the average does not include the modules Division and Prime because the BETA tool did not generate tests with the strategy BVS for them.

To address the nondeterminism of the algorithms/tools, we again generated test cases with 5 variations in the order of parameters and values, and took into account the average of these 5 assessments for the statistical tests.

This option was activated on this third round to explicitly analyze the influence of randomization on the quality of the generated test cases with respect to the improved experiment where it was not used.

To address the nondeterminism of the algorithms/tools, related to the the ordering input of parameters and values, we generated test cases with 5 variations in the order of parameters and values, and took into account the average of these 5 assessments for the statistical tests.

The models generated were tested with remaining 20% test data and also validated using external data sets from PubChem AID1189 and AID1194, DrugBank drugs and Zinc metabolites data.

Various combinations of the non-lethal gene deletions leading to about 111,628 different double knock-outs were generated and tested with FBA using the same objective function.

To make fair comparison with MetaCluster 5.0 on clustering, we generated testing dataset B with reads sampled from genomes with different coverages.

Prediction was assessed in an independently generated test dataset simulated with the same properties as the training data, but with 200 samples.

We tried to eliminate the threat to the construct validity by including a comparison of the pairwise generation with randomly generated test data, in addition to the mutation scores obtained.