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With the proposed method, required implementing parameters for arbitrary responses can be determined easily, which will bring great convenience for realistic MPF implementation based on existing structures.
However, to estimate the variability in the amount of genetic divergence between populations due to historic processes we conducted coalescent simulations implementing parameters from a recent calibration of the method shown to accurately recreate the patterns of genetic variability between the three major world populations used here [12].
In this study, we enhance our previous method by implementing parameters optimization/tuning mechanism, for optimizing the PSO parameters based on genetic algorithm (GA) in order to increase the classification accuracy.
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This depth depends substantially on lower link (length and link position) and implements parameters (weight and height of hitch point).
Therefore, this program may be very useful in simulating the three-point linkage and implement parameters as per the requirement of depth of operation.
Moreover, the simulation results, together with the implemented parameters, are given and compared to the experimental data for 10 specific plant test runs.
We implemented parameters as described in a recent calibration of the method [12], shown to very well approximate the real data available from the HapMap project.
MCScanX (Wang et al. 2012) was then implemented (parameters set as default except maximum gaps = 40) to detect gene synteny/collinearity and duplicated chromosomal segments.
BASH scripts interact with GENESIS 2.3 to implement parameter sweeps.
Thus, in order to increase the speed of convergence, for the proceeding discussion we do not implement parameter resetting.
On this basis, a technique to implement parameter continuation methods is proposed.
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