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Fig. 1 shows an example of how a local solution is created.
In this paper, with regards to the problem nature, the initial solution is created randomly.
A specific process solution is created from the generic process AND-OR graph by graph-based search.
Using the HM, the new solution is created using the following equations: X_{text{NEW}} = X_{text{OLD}} + B times varepsilon (14) B = {text{UB}} - {text{LB}} (15).
To construct the initial population, each possible solution is created randomly, but at least one feasible solution is added to the initial population.
By producing a "child" solution using the above methods of crossover and mutation, a new solution is created which typically shares many of the characteristics of its "parents".
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The simple solution is creating random numbers on the CPU using any of the many implemented reliable random libraries proposed in [10].
A vitally important part of the solution is creating the "energy to do battle".
Adopting a functional design approach, the fixture design solution was created in two levels: functional and detailed.
In the genetic algorithm, new candidates for the solution are created by two mechanisms i.e. crossover and mutation.
In doctoral research conducted by McConkie et al. in 2013, a solution was created by employing Min-Plus topical algebra instead of pairwise matrix multiplication [11].
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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