Improvement of Genetic Algorithm for Classifying SNP Fragments

Abstract

Reconstructing haplotype in MEC (Minimum Error Correction) model is an important clustering problem which focuses on inferring two haplotypes from SNP fragments (Single Nucleotide Polymorphism) containing gaps and errors. Mutated form of human genome is responsible for genetic diseases which mostly occur in SNP sites. In this paper, genetic algorithm (GA) is considered as a classifier of diploid genomes. New encoding approach is used to improve GA efficiency. In the previous approach of GA based on reconstruction rate, all bits of chromosome considered as cluster state of SNP-fragments. In our proposed method the value of the final haplotypes is based on the centers of SNP fragments clusters. Finally, these two approaches are executed on four standard datasets (ACE, Daly, SIM0 and SIM50) and the results show the efficiency of our proposed approach.