A Quantum Evolutionary Algorithm for Effective Multiple Sequence Alignment

Abstract

This paper describes a novel approach to deal with multiple sequence alignment (MSA). MSA is an essential task in bioinformatics which is at the heart of denser and more complex tasks in biological sequence analysis. MSA problem still attracts researcher's attention despite the significant research effort spent to solve it. We propose in this paper a quantum evolutionary algorithm to improve solutions given by CLUSTALX package. The contribution consists in defining an appropriate representation scheme that allows applying successfully on MSA problem some quantum computing principles like qubit representation and superposition of states. This representation scheme is embedded within an evolutionary algorithm leading to an efficient hybrid framework which achieves better balance between exploration and exploitation capabilities of the search process. Experiments on a wide range of data sets have shown the effectiveness of the proposed framework and its ability to improve by many orders of magnitude the CLUSTALX's solutions.

Quantum-inspired evolutionary algorithms: a survey and empirical study

“…It uses crossover and mutation operators to replace the bQIEAo migration operators. According to the bQIEAcm reported in Li et al (2005a), Xu et al (2005), Meshoul et al (2005aMeshoul et al ( , 2005b, Wang et al (2005c), Yang et al (2004aYang et al ( , 2004bYang et al ( , 2005; Talbi et al (2004aTalbi et al ( , 2004bTalbi et al ( , 2004c, Li and Zhuang (2002), Abdesslem et al (2006), Yang and Jiao (2003), Guo et al (2007), Yang and Ding (2007), Shu (2007), Wei et al (2008), Ding et al (2008), Zhao et al (2009), the pseudocode algorithm can be summarized as shown in Fig. 6.…”

“…In Abdesslem et al (2006), Meshoul et al (2005a), the bQIEAcm was successfully applied to solve a multiple sequence alignment problem, which is a well-known NPhard combinatorial optimization problem in bioinformatics (Wang and Jiang 1994). Experiments were conducted on two benchmarks with 24 data sets (Thompson et al 1999;Gardner et al 2005).…”

“…(2005a),Xu et al (2005),Meshoul et al (2005a),Yang and Ding (2007),Abdesslem et al (2006),Meshoul et al (2005b),Wang et al (2005c),…”

A quantum-inspired gravitational search algorithm for binary encoded optimization problems

“…Many researchers have improved the original QEIA (BQEIAo) proposed by Han and Kim where, different types of crossover and mutation operators have been suggested to integrate with BQEIAo (Talbi et al, 2004;Yang et al, 2004;Meshoul et al, 2005;Shu, 2007). It is worth noticed that the mutation and crossover operators are different from classic ones because these operators should be performed on Q-bit individuals, hence; they are called quantum crossover and quantum mutation.…”

“…where the structure of ith Q-bit object is (2010) MOQEA Talbi et al (2004) Image registration Hybridization of BQEIAo with GA Yang et al (2004) Multi-user detection in DS-CDMA Different types of crossover/mutation operators Meshoul et al (2005) Multiple sequence alignment Shu (2007) Optimal resource allocation Wang et al (2005) Function optimization Li et al (2004) Machine learning Hybridization of BQEIAo with immune system Bi and Jin (2007) Image segmentation Niu et al (2009) Flow shop Hongjian et al (2009) In standard GSA, to compute the velocity of an object, total forces from a set of heavier objects that apply on it, are considered based on the combination of laws of gravity and motion (Eqs. (12) and (13)).…”