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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.
Items in the organizational culture questionnaire were generated, tested and validated according to a rigorous process.
Each individual GARP model is a set of 50 rules that are randomly generated, tested and modified.
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We generated test graphs of 512 nodes.
Burke et al. (2004): Burke generated test instance to test the best-fit (BF) algorithm.
During fuzzing, the quality of actually generated test cases is constantly forwarded to the test case generating Lévy flights.
Each boxplot depicts the performance on the 10 randomly generated test sets.
The automatically generated test scripts were based on the abstract models (abstract B machines).
Each boxplot visualizes the performance on the 10 randomly generated test splits.
To satisfy both criteria can improve the quality of the generated test data.
This experiment is performed by using the all 60 generated test problems.
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