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The algorithm combines genetic and greedy methods.
The proposed method consumes significantly less dynamic power compared to such greedy methods.
The algorithms of these greedy methods (MP, OMP, and CoSaMP) are shown in Table 4.
Edmonds [8] uses this to develop greedy methods for such optimization problems.
However, they are less severely affected by shadow and concavity on the background than greedy methods.
The concepts of this method are the basis of other advanced greedy methods such as OMP [40] and CoSaMP [41].
Similar(30)
The greedy method is as follows.
In the greedy group gene selection approach, we followed a greedy method used in [4].
A dynamic programming method and a low-cost greedy method are developed to solve the problem.
In this paper, a greedy method is proposed to get the optimized test set.
First, a greedy method which obtains good approximations in short computational times is proposed.
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