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Figure 2 | BMC Bioinformatics

Figure 2

From: MetaPIGA v2.0: maximum likelihood large phylogeny estimation using the metapopulation genetic algorithm and other stochastic heuristics

Figure 2

The MetaPIGA-2.0 model setting window. The user can choose a substitution model and set the corresponding parameters: here, the GTR model (and estimated starting values of the rate matrix) with rate heterogeneity (discrete Gamma model) and no proportion of invariable sites has been selected automatically after performing a likelihood ratio test (lower left buttons). The user can also choose how and when intra-step optimization of target parameters (here, branch lengths, rate matrix parameters, and the alpha shape parameter of the Gamma distribution) will be performed (here, at the end of the search, using a genetic algorithm). Note that, as the metaGA is a stochastic heuristic, most of the parameters optimization occurs inter-step, i.e., across generations under the effect of operators (see Figure 3).

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