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Finally, multifunctionality ranking may be a too-stringent control (when using ROC) because it is literally optimized and performs better than many real algorithms on actual data ; rather, the correlation of the "real" prediction results with multifunctionality ranking is often a more helpful measure.
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The conclusions obtained in the theoretical framework are confirmed in a real multi-robot collective gathering task using one of the many real embodied evolution based algorithms and showing that two canonical parameters are the most relevant in terms of specialization for this type of algorithms.
Artificial neural networks (ANN) recognized for representing the complex relationships and genetic algorithms (GA) are successful for optimization of many real world problems.
Many real motifs contain positions of low information (gaps), but the existing algorithms usually do not allow gaps, or require a user to specify the exact location and length of gaps, which is often impractical for real applications.
A good tracking algorithm should be able to work well in many real circumstances, such as background clutters, occlusions, and different illuminations [1, 2].
Many real made guys frequented his store.
Many real networks are directed.
Lincoln has just too many "real" names.
Many real genomes have highly repetitive regions.
SI algorithms have provided effective solutions to many real-world type optimization problems, that are NP-Hard in nature.
Many real-world network applications can be modeled as knapsack problems and solved by approximate algorithms.
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