Sequence Alignment By Modified Teaching Learning Based Optimization Algorithm (M-TLBO)

Abstract

Sequence alignment is a most important first step to a wide variety of analyses that can be performed on the biological sequences like DNA, RNA or protein. Sequence alignment is a daily practice of many biologists to determine the similarity among biological sequences. It is considered as an optimization problem. Researchers developed many meta-heuristic optimization algorithms inspired by nature to produce optimal alignment. In all these heuristic algorithms mutation and crossover are the most prominent steps. Every algorithm is having different criterion for mutation and crossover operations. Recently in 2011, R.V. Rao and et al. proposed a new algorithm called Teaching Learning Based Optimization algorithm (TLBO) to deal with constrained and unconstrained optimization problems. This paper uses TLBO to solve the sequence alignment problem and also proposes a new optimization algorithm called Modified-TLBO (M-TLBO). Both the algorithms, TLBO & M-TLBO are analysed by conducting experiments with bench mark data sets from “prefab4ref” & “oxbench” and observed that the newly proposed algorithm M-TLBO outperformed TLBO in solving the sequence alignment problem by producing the best fitness scores in reduced computational time.