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To find an optimal set of chromosomes, we define an optimization problem over a chromosome graph.
Still, using a Silicon Graphics Origin 2000 supercomputer with 32 processors it took more than two weeks of continuous operation to find an optimal set of parameters.
There is no "optimal" set of tools.
Work [27] investigates finding the optimal set of landmarks.
The machine learning algorithm selects the optimal set of attributes with high classification accuracy.
We use an iterative search algorithm to find the optimal set of λ k and μ.
The effectiveness could be further increased if an optimal set of weights could be found.
The question designing strategy of [20] is to generate the optimal set of questions.
The optimal set of shifts for RM 1,6) are given in Table 3.
A more optimal set of parameters from the reconstructed spectrum will be derived.
({mathcal{T}}^*) denotes the optimal set of subgraph patterns ({mathcal{T}}^*subseteq {mathcal{S}}).
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