GENETIC PROGRAMMING FOR MAXIMUM-LIKELIHOOD PHYLOGENY INFERENCE

Abstract

Phylogeny reconstruction is a difficult computational problem, because the number of possible solutions increases with the number of included taxa. For this reason, phylogenetic inference methods commonly use clustering algorithms or heuristic search strategies to minimize the amount of time spent evaluating non-optimal trees. Even heuristic searches can be painfully slow, especially when computationally intensive optimality criteria such as maximum likelihood are used. I describe here a genetic programming to heuristic searching that can tremendously reduce the time required for maximum-likelihood phylogenetic inference, especially for data sets involving large numbers of taxa, and we confirm its availability by experiments.