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If the best candidate assignment is better than (i.e. has a higher fitness value than) the current assignment then the candidate assignment should replace the current one.
The candidate assignment (a), represents an assignment that might or might not be adopted.
Algorithm 1 depends on two assignments, the current assignment and the candidate assignment.
Algorithm 2 calculates the expected cost of energy due to the candidate assignment during the time interval t.
Algorithm 1 uses the captured monitoring information to calculate the CPU utilization of each of the PMs in the candidate assignment.
The key idea is passing the candidate assignment a as a parameter to the EstimatedUtilization(a) function for computing the expected utilization due to the assignment.
Similar(52)
The test screens out unlikely candidate assignments to build disjoint candidate assignments and put them into separate assignment subproblems.
Finding a good assignment that maximizes the utility, fitness, function involves searching candidate assignments.
Algorithm 1 estimates the CPU utilization of the PM based on the candidate assignments.
The genetic algorithm produces a number of candidate assignments that need to be compared to the current one.
Check out some of the other candidates' assignments, like Getting Sarah Palin Fit For Office, Om-ing For Obama and Anti-Aging Asana for John McCain.
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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