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The optimal objective function values attained by each algorithm is shown in Table 2.
Hence, the number of final non-dominated solutions obtained by each algorithm is important to calculate this metric (Bandyopadhyay et al. 2004).
The total power allocated by each algorithm is shown in Fig. 4. It is observed from the figure, the curves of power consumed by each algorithm are roughly flat except the PSO algorithm.
In case 3, the result comparison of FEs' cost by each algorithm is similar to that in case 2. That is, PSO consumes the least FEs among the four algorithms, though its allocation solution is the worst; PADE costs less FEs than DE, ABC, and jDE; its solution is the best compared with PSO, DE, jDE, and ABC.
The ranking of the biclusters by each algorithm is the ranking generated by the respective package.
We can observe that the average overlapping of the known complexes is above 0.5, and that of the complexes predicted by each algorithm is significantly lower.
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A breakdown of the scores by treatment group for each algorithm is shown in Table 4.
In order tocompare the complexity of all algorithms solving the same problem, the running time ofcomputing of each algorithm is denoted by a function T : N → ( 0, ∞ ] in such a way that T ( n ) represents the time taken by the algorithm to solve theproblem under consideration when the input of the algorithm is of size n.
These studies evidenced how each algorithm is characterized by peculiar overall performances and tolerance to errors in the sequences.
Each algorithm is evaluated by using the leave-one-out cross validation paradigm, where each known gene is left out once.
The numbers of clusters found by each algorithm are placed between parentheses.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com