A steady state Genetic Algorithm for Multiple Sequence Alignment
Sabari Pramanik, Sanjit Kumar Setua · 2014
Multiple Sequence Alignment is one of the important research topics in Bioinformatics. The objective is to maximize the similarities among sequences by adding and shuffling gaps in sequences. We here present a genetic algorithm based approach to solve the problem efficiently. We use steady state Genetic Algorithm with a new form of chromosome representation. PAM 350 is used as scoring matrix for calculating the SOP score, which is the fitness score in genetic algorithm. The results are tested using BAliBASE benchmark dataset and it shows that the solution does offer better results.