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The performance of the framework is assessed through applying it to several public microarray data.
The performance of the framework has been validated using experiments on synthetic and real sequence of images.
The performance of the framework is evaluated by coupling Weather Research and Forecasting (WRF) model with three microscale computational fluid dynamics (CFD) codes of varying complexity.
The performance of the framework is illustrated with its application to a number of complex 3D datasets and a quantitative analysis.
The performance of the framework is demonstrated on a case study involving a non-linear exothermic chemical reaction, which is greatly affected by random disturbance signals.
The results show that the performance of the framework was relatively stable during the study period with low variabilities in the r values (1 STD < 0.06) and error measures (1 STD < 0.05).
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Second, we compared the performance of the genetic framework with the exhaustive framework (mutation 0.033).
Finally, we compared the performance of the genetic framework with the exhaustive framework (mutation 0.1).
Second, we compared the performance of the genetic framework with the exhaustive framework (crossover 0.7).
Finally, we compared the performance of the genetic framework with the exhaustive framework (crossover 0.9).
For the crossover operator, we conducted three analyses: first, we compared the performance of the genetic framework with the exhaustive framework (crossover 0.6).
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