MetaPIGA (by Đorđe Grbić, Raphaël Helaers, & Michel C. Milinkovitch) is a robust implementation of several stochastic heuristics for large phylogeny inference (under maximum likelihood). It includes a random-restart hill climbing, a simulated annealing algorithm, a classical genetic algorithm, and the metapopulation genetic algorithm (metaGA). MetaPIGA allows the use of morphological (presence/absence) and molecular (DNA, Proteins) data. It uses standard formats for data sets and trees. Molecular data can be analyzed with complex substitution models (nucleotides, codons, and amino-acids), discrete Gamma rate heterogeneity, and data partition.

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  1. Warnow, Tandy (ed.): Bioinformatics and phylogenetics. Seminal contributions of Bernard Moret (2019)