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The use of 1 mmol L-1 solutions of candidate compounds in a ratio of 5 mL g-1 of soil resulted in the addition of 5 μmol of compounds per g of soil.
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The last accepted candidate solution is then taken as the starting solution for the generation of candidate solutions in the next iteration.
It seeks the optimal solution by evolving a population of candidate solutions over a number of generations or iterations.
Optimal PTS-AIC exhaustively searches for the best weighting factors from the whole solution space of candidates.
The system prepares a test solution by mixing a 15N-labeled protein solution and a solution of each candidate compound, loads it to a flow cell-type NMR probe, and recycles the protein after the data acquisition.
As a directed random search technique, AGA adopts natural evolution strategy and finds out the solution by continually evolving a population of candidate solutions [32], and the evolution process mainly includes selection, crossover, and mutation.
A search problem is one in which optimal or near optimal solutions are sought in a search space of candidate solutions [4].
So, iteratively we will obtain a set of candidate solutions and from those candidate solutions we shall choose a satisfactory solution using the criteria developed.
One of the most important of these challenges is user fatigue which is caused by the evaluation of candidate solutions (where each solution represents a candidate design) in each algorithm iteration [4, 5].
It should be noted that the solutions obtained by a genetic algorithm are usually sub-optimal and different solutions can be obtained given a different starting population of candidate solutions.
The new algorithm is shown to present a better behavior, achieving a better set of candidate solutions, which dominate the solution set of the former algorithm.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com