Computing consensus trees amounts to finding a single tree that
summarizes a collection of trees. Three evolutionary algorithms
are defined for this problem, featuring characteristics of genetic
programming (GP), evolution strategies (ES) and evolutionary
programming (EP) respectively. These algorithms are evaluated on a
benchmark composed of phylogenetic trees computed from genomic
data. The GP-like algorithm is shown to provide better results
than the other evolutionary algorithms, and than two greedy
heuristics defined ad hoc for this problem.