Section 3 presents the analytical formulation of the problem and the algorithm of the proposed approach.
In this case, the algorithm of the two-stage approach is applied with m = 5.
All the algorithms of the topological approach were implemented in Python.
To configure the algorithms of approach SBA, besides the parameters related to the evolution process, it was also necessary to set the weights of the measures: attribute and operation coupling to compose the aggregated fitness function.
Through the parametric study of crossover and mutation probabilities of the optimization algorithm, the results of the approach are generally satisfactory.
Figures 3, 4, and 5 present the pseudocodes of the knapsack algorithm, the fuzzy approach, and the GRAMAKA scheme for scheduling, respectively.
The experimental results on a variety of workloads show that the algorithms outperform the state-of-the-art approaches.
By use of a direct feedback mechanism that is seamlessly consistent with the search strategy of the greedy algorithm, the proposed approach is capable of inheriting the simplicity and efficiency of that algorithm and performing competitively with related snakes.
EM algorithms are currently the state-of-the-art approach to transcriptome quantification from RNA-Seq read, and have been proven to outperform count-based approaches.
An estimation of the approximation error, the corresponding algorithm of our approach and some illustrative examples are presented.
