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基于支持向量机性能预测的量子遗传网络任务调度研究的综述报告.docx

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基于支持向量机性能预测的量子遗传网络任务调度研究的综述报告 Quantumgeneticneuralnetwork(QGNN)isanintelligentmethodusedforsolvingcomplexoptimizationproblemssuchastaskscheduling.Thisapproachincorporatestheprinciplesofquantumgeneticalgorithmsandneuralnetworktechniques.ThegoalofQGNNistoachieveanoptimalsolutionforthetaskschedulingproblem.Theperformanceofthisapproachdependsontheaccuracyoftheperformancepredictionmodelusedtoevaluatethefitnessofeachsolution. Oneofthemethodsusedtoimprovetheaccuracyoftheperformancepredictionmodelisasupportvectormachine(SVM).SVMisamachinelearningalgorithmthatiscapableofpredictingtheperformanceofaschedulingsolutionbasedonhistoricaldata.TheSVMusesalinearornon-linearkernelfunctiontotransformthedataintoahigh-dimensionalspace,wheretheseparationofdifferentclassesbecomeseasy,andtheoptimalhyperplanecanbedrawn.ThisallowstheSVMtoclassifysolutionsintogoodorbadsolutionsbasedontheirpredictedperformance. TheQGNNapproachinvolvesintegratingtheSVMintotheneuralnetwork.Thisenhancestheaccuracyoftheperformancepredictionmodelandimprovesitsabilitytolearnandsolvecomplexoptimizationproblemsefficiently.TheneuralnetworkformsthebasisoftheQGNNapproach.Itconsistsofasetofinterconnectednodesthatsimulatethefunctionsofbiologicalneurons.Theneuralnetworklearnsfromthehistoricaldataandusesthisknowledgetoprovideoutputvaluesthatareusedtoevaluatethefitnessofeachschedulingsolution. TheQGNNapproachemploysaquantum-inspiredencodingmethodtorepresentthetaskschedulingproblemasabinarystring.Thisrepresentationallowstheuseofquantum-inspiredoperatorssuchascrossover,mutation,andselectiontosolvetheproblem.Thecrossoveroperatorcombinestheparentsolutionstogeneratenewoffspringsolutionswithimprovedfitness.Themutationoperatorintroducesrandomchangestothesolutions,whiletheselectionoperatorchoosesthebestsolutionsanddiscardstheworstones. Inconclusion,theQGNNapproachthatisbasedonSVMperformancepredictionmodelsisapromisingmethodforsolvingcomplexoptimizationproblemssuchastaskscheduling.Theaccuracyoftheperformancepredictionmodeliscriticaltot