Phylogenetic tree reconstruction using self-adaptive genetic algorithm

We have developed a self-adaptive genetic algorithm (GA) for a maximum-likelihood reconstruction of phylogenetic trees using nucleotide sequence data. It resulted in a faster reconstruction of the trees with less computing power and automatic self-adjustment of settings of the optimization algorithm parameters. We focused on the use of GAs with self-adaptive control parameters and GA integration with phylogenetic tree representations. The developed technique is applicable to any nucleotide sequences inferring evolutionary relationships between organisms