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A mapping of the designs generated by the GA as a function of the mass and cost constraint combination reveals that an appropriate distribution of porosity and material benefits the overall thermal performance of the layered porous medium.
For each constraint combination above, GAMETES was used to generate a population of models of sizes 1,000, 10,000, and 100,000 yielding a total of 72 different populations of models.
Specifically, the models generated were more evenly distributed across different shape classes, which simultaneously improved class coverage such that more shape classes were represented within each of the 12 constraint combination populations with variable K than when compared to fixed K.
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Additionally, the constraint spaces are classified into different constraint combinations, with typical position spaces depicted via geometric entities.
Furthermore, the systematic synthesis process based on the constraint combinations and the geometric entities is demonstrated via several examples.
An inductive development methodology provides a means for developing sophisticated algorithms that become increasingly powerful as they are subjected to new constraint combinations.
There were 15 possible constraint combinations.
Certain constraint combinations may yield few or no viable models [ 19].
Thus, a total of 24 constraint combinations were considered (6 heritabilities, ∗ 2 MAFs ∗ 2 prevalence settings).
We use GAMETES to generate differently sized populations of pure, strict, two-locus epistatic models possessing different constraint combinations.
If a row of boxes has no *'s than at least one model was found for the respective shape class no matter which of the 12 constraint combinations were used.
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