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If a solution is not a local optimum, the local search can get a optimal solution in its near space.
Moreover, a local search which can select a optimal solution in the near space of the current solution is incorporated into the GA.
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Finding an optimal solution in a problem requires a costly multicriteria optimization.
In this section, we prove the continuity of a gradient and state a necessary condition to an optimal solution in the variational inequality form using the gradient.
Local search is an algorithm chooses an optimal solution in the near solution space of the current solution, until it reaches a local optimal solution.
Finding an optimal solution in an acceptable time is difficult, even using a powerful computer.
The artificial bee colony algorithm that is innovated in 2005 by Karaboga [ 9] is one of the bioinspired evolutionary techniques, which has been employed to identify an optimal solution in different optimization problems.
In these conditions, even after several hours of running, Lingo cannot yield an optimal solution in a reasonable time.
Learning an optimal solution in a context of interdependence between players is associated with development of reciprocal trust, which in turn mediates transfer of learning across games.
Even though it restricts the problem model, this method is advantageous in that it finds an optimal solution in a reasonable computation time.
Owing to high computational complexity involved in solving these problems, it is not always possible to obtain an optimal solution in a reasonable computation time.
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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