Performance Analysis of Optimization Algorithms in Multiple Nucleotide Sequence Alignment Problem

The ultimate aim of the survey is to outline the characters of popular evolutionary algorithms and compare their performances to solve multiple nucleotide sequence alignment problem in bioinformatics. Bioinformatics is the storage, manipulation and analysis of biological information such as nucleic acid and protein sequences via computer science. Multiple sequence alignment is used to generate a concise, information-rich summary of sequence data in order to make decisions on the relatedness of sequences to a gene family. Optimization algorithms, such as the Genetic Algorithm(GA), Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization(ACO)algorithm and Artificial Bee Colony (ABC)algorithm, can give solutions to multiple nucleotide sequence alignment problems near to the optimum for many applications; however, in some cases, they can suô€€€er from becoming trapped in local optima. The Stem Cells Algorithm (SCA) is an optimization algorithm inspired by the natural behavior of stem cells in evolving themselves into new and improved cells. The SCA avoids the local optima problem successfully. Multiple Sequence alignment provides an effective way to find conserved regulatory patterns in nucleotide sequences which helps in the diagnosis and classification of diseases. Since multiple sequence alignment is an ongoing research area, we intend to analyze and compare the features of optimization algorithm which have its own strengths and weaknesses. The proposed hybrid approach of genetic algorithm with an alignment improver of stem cells algorithm produce better results than other optimization algorithms