Nomadic genetic algorithm for multiple sequence alignment (MSANGA)
Genetic algorithms (GA) are adaptive search procedures that try to produce a globally optimum solution for problems of huge search space. This paper speaks about a variant of the standard genetic algorithm (SGA) called nomadic genetic algorithm (NGA) which is based on the concept of 'birds of the same feather flock together'. This NGA is found to maintain the diversity of individuals in the population by intelligently adapting to its environment as well as results in faster convergence of the solution. The objective of this paper is to prove the merits of NGA over SGA for problems of large search space like the problem of multiple sequence alignment (MSA) in bioinformatics. NGA was applied to MSA (MSANGA) and the convergence of NGA is compared with that of SGA and the results tabulated. Also, the accuracy of the alignment produced using MSANGA is compared with nine other popular tools for the data sets chosen from the standard BaliBASE benchmark alignment suite, illustrating the superiority of NGA over SGA and all other tools to produce quality alignment at a faster rate.
Keywords: genetic algorithms, GAs, multiple sequence alignment, MSA, selection, adaptive search, nomadic genetic algorithm, NGA, bioinformatics