Algorithm of Maximization

The maximization process is based on the variables marker classes represented in a core collection. For each core collection or subcore collection, a score is calculated on marker variables as the number of represented classes. To do it as well, Axij matrix is used by counting values above or equals to 1 for the core collection accessions

While the core collection score is improved, we repeat each step. To avoid perpetual loop (when the score is first incremented then decremented by 1 in each iteration), we define a breaking value for the loop iterations.


Note: AxijFreq is used by optimizing functions for Nei and Shannon indices.

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