MetaPIGA 2 includes a classical genetic algorithm, a simulated annealing algorithm and the metapopulation genetic algorithm (metaGA). MetaPIGA 2, comes in addition with complex substitution models, discrete Gamma rate heterogeneity, and the possibility to partition data.
MetaPIGA 2 also implements the Likelihood Ratio Test, the Akaike Information Criterion, and the Bayesian Information Criterion for automated selection of substitution models that best fit the data.
Heuristics and substitution models are highly customizable through manual batch files and command line processing.
However, MetaPIGA 2 also offers an extensive graphical user interface for parameters setting, generating and running batch files, following run progress, and manipulating result trees.
MetaPIGA 2 uses standard formats for data sets and trees, is platform independent, runs in 32 and 64-bits systems, and takes advantage of multiprocessor and multicore computers.
The metaGA resolves the major problem inherent to classical genetic algorithms by maintaining high inter-population variation even under strong intra-population selection.
Implementation of the metaGA together with additional stochastic heuristics into a single software will allow rigorous optimization of each heuristic as well as a meaningful comparison of performances among these algorithms.
MetaPIGA 2 gives access both to high customization for the phylogeneticist, as well as to an ergonomic interface and functionalities assisting the non-specialist for sound inference of large phylogenetic trees using nucleotide sequences.
Requirements:
· Java 6 or later
What`s New in This Release: [ read full changelog ]
· Resolve an issue that caused protein datasets to crash with Hill Climbing or Simulated Annealing under GTR20 or empirical models.
· Resolve an issue that prevented the `memory settings` tool to correctly update the configuration file under Windows 7/Vista and Linux.
