45560.退火進化算法在生物序列比對中的應用研究

1、碩士學位論文退火進化算法在生物序列比對中的應用研究ApplicationSearchofAnnealingEvolutionAlgorithminBiologySequenceAlignment作者姓名學科、專業(yè)學號指導教師完成日期王蝗讓箕數(shù)堂2絲QlQ2j工克詮2Q豎至6目大連理工大學DalianUniversityofTechnology退火進化算法在生物序列比對中的應用研究ApplicationSearchofAnnealingE

2、volutionAlgorithminBiologySequenceAlignmentAbstractComparisonisthemostcommonmethodinthescientificresearc]1，thatthrou血thecomparisionofresearchobjectwitheachotherrelativelylookforthecharacteristicthattargetmightpossessSequ

3、encealignmentisthemostfrequentlyandclassicalresearchmeallSofbioinformaticsItsmeaningrestwithanalyzingsequenceeomparabilityfromthelevelofnucleicacidandaminoacid，conferringitsrelationofstlucturalfunctionandevolutionthefoun

4、dationofgenediscern，moleculeevolutionandoriginoflifestudyHaveintroducodthecurrentsituationofstudyonalgorithmthatpairwisesequenceandmultiplesequencealignmentinbioinformaticsinthistextThanbasedonsimulatedannealingalgorithm

5、andgeneticalgorithmproposetoallannealingevolutionalgorithminbiologysequencealignmentThealgorithmcomplexityofmul印lesequenceincreasesintheexponentlaw，isaNPproblem，accordingtothecharacteristicweadoptthealgorithmbelongstoite

6、rativealgorithmthatcombinewithsimulatedannealingalgorithmandgeneticalgorithmtreatmentNPproblem也atthiskindofmethodcanbeverygoodBecausegeneticalgorithmcanbeeasytocauseearlymaturingtodisappearquestion，soastoevolutionbeunabl

7、etogetoptimumtosolveAlotofmultiplesequencealignmentbasedongeneticalgorithmhavethisshortcomingmorethanthealgorithmrightAccordingtothischaracteristic，throughintroducingsimulatedannealingalgorithmutilizesimulatedannealingal

8、gorithm，acceptingcriterion(Metropoliscriterion)keepcolonyvarietyofindividualtosolveearly—maturingtodisappearquestionSimulatedannealinghastwo—waysearchabilitythatprobabilityjumpssuddenly，itisapttojumpoutthetrapofsomeextre

9、mevalue，theoveralloptimizationthatcanalsoguaranteetobesearchedforIntroducingsimulatedannealingalgorithminthegeneticalgorithmtactics，notonlyenrichthegeneticalgorithmsearchbehavior，avoiddisappearearlymaturing，butalsocanuti

10、lizestrongtheoveralloptimizationofgeneticalgorithmrunningsidebysideoftosearchforabilityThistextcarriedonaseriesofdatatestfinally，passing也etestresultwecanseethisalgorithmisimprovedinbiologicalsensitivenessandoperationeffi