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A genetic algorithm [67, 68] is a meta-heuristic based on the application of a computational simplification of the biological evolutionary model over binary representations of solutions of a combinatorial optimization problem.
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To facilitate analysis of the distribution of log likelihood ratios, we consider a computational simplification, namely assuming all chromosomes have the same length rather than ranging over five different lengths.
Furthermore, an appealing computational simplification is obtained due to the particular form taken by the Gohberg-Heinig formula.
The five-state model therefore allows substantial computational simplification while preserving sufficient detail for our main goal of long-range haplotype phasing.
For computational simplification, the P and Q matrices are not directly found but combined and represented by an H matrix.
A model is a simplification of a real system.
Several related aspects are also discussed, including: computational simplifications for efficient implementation; evaluation of computational complexity; CFO and STO compensation and channel equalization.
From a computational perspective, this simplification decreases the complexity of processing because it uses sum operations instead of multiplications, while subtractions replace divisions [13, 20].
"That is a distortion and a simplification of the matter.
Instead, the modeler can concentrate on a computational description of the process and leave the construction and simplification of expressions to the computer.
In turn; this results in mitigation of the computational requirements and in a simplification of the filter implementation by the engineers.
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