求解隐式目标优化问题的交互式进化算法分析-interactive evolutionary algorithm analysis for solving implicit objective optimization problems.docxVIP

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求解隐式目标优化问题的交互式进化算法分析-interactive evolutionary algorithm analysis for solving implicit objective optimization problems.docx

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求解隐式目标优化问题的交互式进化算法分析-interactive evolutionary algorithm analysis for solving implicit objective optimization problems

convergencetimeandthereforerealizeuserfatiguereduction.Weproposeanactiveinterventionoperator:asub-chromosomefixingoperator.Thisoperatorcanpreventthegoodgenesegmentsfrombeingdestroyedbycrossoverandmutationoperators,andcanreducethesearchspaceremarkably.Traditionalcrossoverandmutationoperatorsmaybecomeineffectiveasaresultoftheproposedoperator,soweimprovethecrossoverandmutationoperatorstomakethemeffectiveeveniftheproposedoperatorisperformed.Atlast,twointeractivegeneticalgorithms,onewiththeproposedoperatorandtheotherwithout,areappliedtoafashiondesignsystem,respectively.Experimentalresultsdemonstratethatthesub-chromosomefixingoperatorcanacceleratetheconvergenceofIECandimprovethequalityofsolutions.Weproposeamulti-userinteractivegeneticalgorithm(MUIGA)thatcanmeettherequirementsofmulti-people.SingleuserevaluationisreplacedbygroupevaluationinMUIGA.Thatistosay,theindividuals’fitnessvalueslieonthedecisionofagroupinsteadofasingleuser.Thedetaildesignofthethreekeymodules—populationinitializationmodule,single-populationmoduleandmulti-populationmoduleisgiven.Atlast,theproposedalgorithmandthegeneralsingle-userinteractivegeneticalgorithmareappliedtoafashiondesignsystem,respectively.ExperimentalresultsshowthatMUIGAcanmeettherequirementsofmulti-peoplebetterthansingle-userIGA.Aninteractivepopulation-basedincrementallearning(IPBIL)algorithmhasbeenproposedtooptimizeproblemswithimplicitobjectives,whicharetraditionallysolvedbyusinginteractiveevolutionarycomputation(IEC).Thatisexpectedtoreduceuserfatigue,whichisakeylimitationofIEC,becauseusersonlyneedtoselectsomegoodindividualsratherthanevaluateallindividualswhenusingIPBIL.TocomparetheperformanceofIECandIPBIL,theyareappliedtoafashiondesignsystem.ExperimentalresultsindicatethatalthoughIPBILneedsmoregenerationstofindasatisfactorydesign,itneedslesstimeconsumptionandmuchfewermouseclicksthanIEC.Accordingly,comparedwithIEC,IPBILcansignificantlyreduceuserfatigue.Tosolvemultimodaloptimizationproblemswithimplicitobjectives,weproposeanalgorithm—IPBILwi