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The value normally called the fitness indicates the goodness of the solution.
The measure of the goodness of the solution is the average misfit rotation angle, which we found to be 2.0°.
Each seed is assessed according to its fitness value, which is obtained from the fitness function defined to represent the goodness of the solution.
Since the exact optimal solution to this problem is not known (note that no existing methods guarantee finding the global optimum for a large RCPSP), it is difficult to prove the goodness of the solution found by the proposed Simulated Annealing algorithm.
The fitness of a vector indicates the degree of goodness of the solution, which is defined by J m.
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Unlike other decision-making models, the use of this last value gives an idea of the goodness of the result depending on whether the solution obtained is close to the reference value.
The goodness of the analytical solutions is tested by comparison with the simulated curves obtained by numerical integration.
The goodness of the analytical solutions has been tested by comparison with the simulated curves obtained by numerical integration.
To measure the goodness of the result, we calculate the centroid index (CI) against the ground truth (or reference) solution.
To evaluate the goodness of candidate solutions, the execution of the optimization algorithm is combined with a static profiling methodology using several well-known simulation tools.
The goodness-of-fit of the solution revealed that about 90% of the variability in the data could be explained.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com