  The amount of completely sequenced chloroplast genomes increases rapidly
every day, leading to the possibility to build large scale phylogenetic trees
of plant species. Considering a subset of close plant species defined according
to their chloroplasts, the phylogenetic tree that can be inferred by their core
genes is not necessarily well supported, due to the possible occurrence of
"problematic" genes (i.e., homoplasy, incomplete lineage sorting, horizontal
gene transfers, etc.) which may blur phylogenetic signal. However, a
trustworthy phylogenetic tree can still be obtained if the number of
problematic genes is low, the problem being to determine the largest subset of
core genes that produces the best supported tree. To discard problematic genes
and due to the overwhelming number of possible combinations, we propose an
hybrid approach that embeds both genetic algorithms and statistical tests.
Given a set of organisms, the result is a pipeline of many stages for the
production of well supported phylogenetic trees. The proposal has been applied
to different cases of plant families, leading to encouraging results for these
families.
