Dissertation
Dissertation > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory

Quantum Genetic Algorithm and Its Application in Multiple Sequence Alignment

Author XieQiaoZuo
Tutor HuoHongWei
School Xi'an University of Electronic Science and Technology
Course Computer Software and Theory
Keywords Quantum Genetic Algorithm Multiple sequence alignment Probability coding COFFEE
CLC TP18
Type Master's thesis
Year 2008
Downloads 110
Quotes 0
Download Dissertation

Biological information in the multiple sequence alignment problem , is forecast , the interaction between the probe sequence on the basis of phylogenetic tree , the function of genes and proteins . However , the multiple sequence alignment problem is an NP-hard problem , very challenging , sequence alignment algorithm speed and quality requirements . Some intelligent optimization method is applied in the field , and achieved good results . The genetic algorithm is a random iterative optimization algorithm for solving complex combinatorial optimization problems advantage . However, due to the multiple sequence alignment problem itself, the complexity of traditional genetic algorithm convergence rate is relatively slow , there is no guidance on the individual to be corrected with a certain blindness , prone to degradation phenomena . Inspired by the characteristics of the quantum , this paper presents a new quantum the genetic algorithm QGAlign solve biological sequence analysis of the multiple sequence alignment problem . The algorithm was first proposed a new quantum probability encoding method and design of the rotation angle based on the encoding method of quantum mutation operator and five genetic operators . The quantum superposition characteristic chromosome coding , and enhance the diversity of the population ; quantum revolving door through optimal solution to guide the evolutionary process of the groups , and to accelerate the speed of convergence of the algorithm . In order to avoid the quantum rotation gate variability may bring the problem of local optimum design of genetic operators based on the multiple sequence alignment problem , and to optimize the results of comparison , the evolutionary process more enlightening and diversity . Through experimental validation of a genetic operators optimize the performance of the algorithm . The data in BAliBASE2.0 library test compared with CLUSTAL X, SAGA other methods , the results showed that the framework of the algorithm has good ability of global optimization , and has a small population , the fewer the number of iterations , the feasible .

Related Dissertations
More Dissertations