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Application Research on Biology Multiple Sequence Alignment Based on Genetic Simulated Annealing Algorithm
Author: XiangChangSheng
Tutor: ZhouJianJun
School: Hunan Agricultural University
Course: Biophysics
Keywords: Multiple sequence alignment Simulated annealing algorithm Genetic Algorithms Simulated Annealing Genetic Algorithm
CLC: TP18
Type: Master's thesis
Year: 2008
Downloads: 116
Quote: 1
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Abstract
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Sequence alignment is an important basic bioinformatics research topics , one of the most basic tasks for multiple sequence alignment , there is no generic best multiple sequence alignment algorithm . This paper presents a combination of genetic algorithm and simulated annealing algorithm to solve the multiple sequence alignment problem , and this in-depth study and discussion , the main findings are as follows : 1, the analysis of the traditional genetic algorithm ( Genetic Algorithm ) , simulated annealing algorithm ( the Simulated AnnealingAlgorithm ) advantages and disadvantages based on simulated annealing algorithm is introduced genetic algorithm selection strategy and survival strategies , simulated annealing algorithm to reduce the selection pressure of the genetic algorithm , the use of the Boltzman control of the simulated annealing algorithm to receive crossover and mutation individual to build a simulated annealing genetic algorithm . 2, simulated annealing genetic algorithm applied to the multiple sequence alignment problem in multiple sequence alignment simulated annealing genetic algorithm ( MSA - GASA ) mathematical model , the use of MSA - GASA carried the BAliBASE database data set test , the test results of the test and the results of the ClustalX comparative analysis , the results showed that the MSA - GASA than ClustalX than to the results of accuracy higher, with better sensitivity compared with traditional genetic algorithm , MSA - GASA convergence corresponding to speed up the time complexity is relatively small , in short, the MSA - Gasa improve the quality of the multiple sequence alignment , improve the stability of the algorithm .
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