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Genetic programming, a specialization of genetic algorithms, is an evolutionary algorithm-based methodology that uses concepts of survival of the fittest for evolving a fit solution from an original population of random solutions [ 37].
We designed a methodology that ensures that there exists a fit solution (CRM) for the target pattern under either function model, with and without the ubiquitous activator, so that any difference in time-to-evolve can be attributed to the evolutionary ramifications of the ubiquitous activator (see Materials and Methods).
We speculate that this may be due to two complementary reasons: 1) Ubiquitous activators provide alternative solutions to the underlying combinatorial optimization problem of finding a fit CRM and 2) ubiquitous activators reduce the number of binding sites necessary to create certain expression patterns, and thus the number of steps (mutations) needed to find a fit solution.
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The goal of a startup is to build a scalable solution to a general problem facing an industry, not a fitted solution to one company's challenges.
On the one hand, this is interesting because different application's scenarios could have a fittest solution but on the other it can also cause some complexity to select the best technique to address at each state of the database.
Then, it was observed that exponential PSO-DEM provided the best fit solution than a linear form due to the fluctuations of the economic indicators.
The scores are the product of the best fit solution within a cluster, predicted using GRAMM, by the size of the cluster within the 20 highest score predictions.
There are native components for apps on iOS, Android, Windows 8 and more to make sure that publications are delivered best on each platform where they appear, rather than seeming shoe-horned into a best-fit solution.
Thus the approximate solutions are useful to both quickly fit solutions to a particular set of process parameters and to investigate the influence of changing a particular parameter on the system.
Our interpretation is in agreement with theories of evolutionary computation (Holland 1975; Goldberg 1989, 2002), according to which if a combinatorial problem has many fit solutions we are more likely to find one of these solutions quickly (Goldberg 2002).
The 32 attitude statements produced a 5-factor best-fit solution, which accounted for 38.5% of the score variance (Table A1).
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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