An improved genetic algorithm for multiple sequence alignment

Abstract

Multiple sequence alignment (MSA) is one of the most essential tools in bioinformatics. Genetic algorithm is used to simulate biological multiple sequence alignment problem, the initial population and crossover is the most critical part of the genetic algorithm. In this paper, we construct three initial populations and a simple horizontal crossover with respect to the vertical crossover. The experimental results showed that the initial population adding an appropriate proportion MAFFT excellent seed can optimize the population, horizontal crossover can reduce the computing time and the computational complexity. Combination of those two methods can improve the computational efficiency of multiple sequence alignment.