Background: One of the important goals of phylogenetic studies is the estimation of
species-level phylogeny. A phylogenetic tree is an evolutionary classification of different species
of creatures. There are several methods to generate such trees, where each method may produce a
number of different trees for the species. By choosing the same proteins of all species, it is
possible that the topology and arrangement of trees would be different.
Objective: There are methods by which biologists summarize different phylogenetic trees to a tree,
called consensus tree. A consensus method deals with the combination of gene trees to estimate a
species tree. As the phylogenetic trees grow and their number is increased, estimating a consensus
tree based on the species-level phylogenetic trees becomes a challenge.
Method: The current study aims at using the Imperialist Competitive Algorithm (ICA) to estimate
bifurcating consensus trees. Evolutionary algorithms like ICA are suitable to resolve problems
with the large space of candidate solutions.
Results: The obtained consensus tree has more similarity to the native phylogenetic tree than
Conclusion: The proposed method enjoys mechanisms and policies that enable us more than other
evolutionary algorithms in tuning the proposed algorithm. Thanks to these policies and the
mechanisms, the algorithm enjoyed efficiently in obtaining the optimum consensus tree. The
algorithm increased the possibility of selecting an optimum solution by imposing some changes in