Using Harmony Search for Solving a Typical Bioinformatics Problem

Recently, there has been great interest in Bioinformatics among researches from various disciplines such as computer science, mathematics, statistics and artificial intelligence. Bioinformatics mainly deals with solving biological problems at molecular levels. One of the classic problems of bioinformatics which has gain a lot attention lately is Haplotyping, the goal of which is categorizing SNP-fragments into two clusters and deducing a haplotype for each. Since the problem is proved to be NP-hard, several computational and heuristic methods have addressed the problem seeking feasible answers. In this paper, harmony search (HS) is considered as a clustering approach. Extensive computational experiments indicate that the designed HS algorithm achieves a higher accuracy than the genetic algorithm (GA) and particle swarm optimization (PSO) to the MEC model in most cases.