Ant Colony Optimization Genetic Algorithm Is Applied To Sequence Alignment In Bioinformatics

Bioinformatics is an interdisciplinary which uses computers as a main tool to deal with exponential growing biologic data.Sequence alignment (SA) is a basic research approach in bioinformatics, and the structure, functions and evolutional relation of gene can be concluded by sequence alignment.Ant colony optimization (ACO) is a simulative algorithm, through which a better result can be found by imitating the ants that find the shortest path during hunting food.Genetic algorithm (GA) is another simulative algorithm which imitates the process of biology heredity to get the best result.Ant colony optimization genetic algorithm (ACOGA) is a mixed algorithm,which gains the global best result and quickens the rate of convergence through optimizing a group of parameters of ACO via GA.Basing on the analysis of the domestic and overseas sequence alignments, by applying ACO to SA, the paper designed a new ant colony optimization sequence alignment (ACOSA). But ACO usually only gets the best result locally, and by applying GA to ACOSA, this phenomenon can be overcome, and so ant colony optimization genetic algorithm sequence alignment (ACOGASA) comes into being. ACOGASA can enlarge the search space, overcome the local result and get the best result. The experiment indicates ACOGASA can obviously improve the effect of the sequence alignment.