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We also provide a detailed comparison between these algorithms in terms of complexity and performance.
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The main difference between these algorithms lies in the formulation of the objective function and the strategy for optimisation.
This affirmation is based on the fact that the differences between these algorithms are in the optimization process, always aiming at a shorter processing time and lower use of resources, such as computational memory.
Performance comparisons between these algorithms are presented in Figs. 4 and 5.
The second subsection gives the comparison between these two algorithms in terms of achievable performance and computational complexity.
Practitioners can choose between the algorithms in the second group of methods depending on the requirements of their applications and available computational resources.
The performance differences between the algorithms in the high SNR regime can be well understood when we look at the accuracy of the algorithms measured with the standard deviation of received SNRs.
In general there was good agreement between the algorithms in predicting the direction of change when compared with the experimental data.
A closer investigation of the binding site detections in the STAT6 data revealed the more practical differences between the algorithms in real applications.
Statistical significance of the observed differences between the algorithms in their semantic similarity scores was assessed using the paired t-test, where the p-value is calculated by the means of the two-tailed Student's t-distribution.
However, even though NSGA-II is better in some scenarios and MOCell in others, the magnitude of the differences in time between these algorithms was small.
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