Authors: Muhammad Ishaq Afridi
Addresses: IQRA National University Peshawar, Pakistan
Abstract: The family of evolutionary or genetic algorithms is used in various fields of bioinformatics. Genetic algorithms (GAs) can be used for simultaneous comparison of a large pool of DNA or protein sequences. This article explains how the GA is used in combination with other methods like the progressive multiple sequence alignment strategy to get an optimal multiple sequence alignment (MSA). Optimal MSA get much importance in the field of bioinformatics and some other related disciplines. Evolutionary algorithms evolve and improve their performance. In this optimisation, the initial pair-wise alignment is achieved through a progressive method and then a good objective function is used to select and align more alignments and profiles. Child and subpopulation initialisation is based upon changes in the probability of similarity or the distance matrix of the alignment population. In this genetic algorithm, optimisation of mutation, crossover and migration in the population of candidate solution reflect events of natural organic evolution.
Keywords: hybrid genetic algorithms; hybrid GAs; progressive multiple sequence alignment; objective function; evolutionary algorithms; child population; similarity probability; distance matrix; mutation; crossover; migration; candidate solution; organic evolution; bioinformatics.
International Journal of Bioinformatics Research and Applications, 2013 Vol.9 No.6, pp.614 - 624
Available online: 10 Aug 2013 *Full-text access for editors Access for subscribers Purchase this article Comment on this article