Multiple sequence alignment based on genetic algorithms with new chromosomes representation

Multiple sequence alignment is one of the important research topics of bioinformatics. The objective is to maximize the similarities between them by adding and shuffling gaps. We propose a hybrid algorithm based on genetic (GAs) and 2-optimal algorithms. We are using permutation coding corresponding to represent the solution, and we are studying scoring function for multiple alignments, that is used as fitness function. Our GA is implemented with two selections strategies and different crossovers. The probability of crossover and mutation are set as one. Performance and comparison of the proposed GA is analyzed and the obtained solution qualities are reported.