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Table 3 shows the results of genetic algorithm output for 20 times running of the algorithm.
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See Figure 3 for an example run of the algorithm.
In each successfully run of the algorithm, the fitness function is calculated according to individuals.
So, the function g gives an asymptotic upper bound of the running time f and, thus, an 'approximate' information of the running time of the algorithm.
When the problem sustains the standard VMT settings, the running time of the algorithm is Θ(M(|| s||)), where M n) is the running time of the corresponding matrix multiplication algorithm over two n × n matrices.
The running time of the algorithm is also bounded above by a polynomial in and.
Hence, the running time of the algorithm is determined to be ( Oleft( {n + B} right) ).
Total running time of the algorithm is only related to a user-defined error tolerance.
The running time of the algorithm proposed in [16] is linear in the number of image pixels.
Timing measurements aimed at registering the running time of the algorithm on high resolution real life images.
The running time of the algorithm is linear in n.
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