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The code used for solving the examples in the next section is published in (Abali 2015) under GNU public license as declared in (Gnu Public 2007).
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The results of solving the example with NSGA-II are presented as a Pareto front in Fig. 10.
In solving the example of paper, generalized Gaussian quadrature formula and conditional Monte Carlo simulation have been used, respectively.
Two different soft-computing tools (MATLAB and LINGO-11.0) have been used to solve the examples.
A set of MATLAB routines for the implementation of the method as well as sample code used to solve the examples have been developed.
We solved the Example 4.1 using the method described for N = 2 and N = 3. Results are shown in Table 2.
We coded the proposed GA solution method by MATLAB 2010 and applied the program to the same numerical example solved by the LINGO Software using the same desktop PC mentioned above and discussed in "Solving the model with LINGO software".
The solutions are illustrated by solving the same examples from Abad's paper to illustrate the accuracy and completeness of our procedure.
To examine differences between comprehension and production tasks, we also studied the effects of learning by solving the same example.
In the same way, this study confirmed that solving the given example did not effectively transfer to posing new problems.
Table 4 shows two examples to solve the model using a software and using different data in which descending cost trend can be observed.
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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