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The optimal results were found by exhaustive exploration and are obtained by selecting the solution with the lowest cost (Energy × Cycles) in exploration space, for each application.
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A specific solution with less crowding distance means less solution density around the specific solution, so for selecting the solutions for the next generation, the more crowding distance is better than less crowding distance (Deb 2001).
According to Bent and Van Hentenryck [25], this selection should be performed using a consensus function to select the solution that is most similar to all other solutions.
Select the solution that makes the most sense for your application.
Then a decision-making tool (M-TOPSIS) selects the solution providing the best compromise.
The tester can select the solution that better fits his/her needs.
The user needs to select the solution that better covers his/her requirements and needs.
We select the solution which requires the least modifications to the i2b2 evaluation framework and illustrate it with our system.
The designer can subsequently select the solution with the best compromise between the objective functions for the specific application and constraints.
The designer can select the solution that most appropriate to the requirements from the set of solutions obtained, studying for each solution its IC, CC and ACS.
Uniqueness, however, is imposed by an additional requirement which is to select the solution that causes a minimal degradation of the previous network knowledge.
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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