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We developed a genetic algorithm (GA-MCS) to quickly find many (near) optimal intervention strategies while overcoming the above mentioned computational burden.
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The proposed hardware architecture splits the computational burden into several processes where calculations of mentioned object's features are made simultaneously in order to improve system's speed.
Computing this factor z t results in heavy computational burden for normalization.
The above-mentioned methods suffer from either excessive memory requirement or huge computational burden.
As it was mentioned, Figure 7 shows that increasing K from 2M + N max − N min − 1 causes no improvement in relative errors while it makes additional computational burden.
Moreover, it increases computational burden.
The computational burden is thus significantly lower.
This might unnecessarily increase computational burden.
However, it suffers from computational burden.
This further increases the computational burden.
Section 3 analyzes the computational burden.
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