简介：Healthcareconsumersnolongerwanttobepassivereceiversoftreatmentandcare,theywanttobetohealthinformation,thankslargelytotheInternet,andactivehealthconsumerorganizationshaveprovidedconsumerswithenablingindependenthealthinformation.Individualconsumersarealsomorewillingtoenterintoadialogue,participateandinfluencedecisionsconcerningtheirowncare.Atthesametimetheconsumerrolebecomesmorediverse.Populationsinmanycountriestodayareculturally,socially,anddemographicallydiverse.Understandingconsumerdiversityisoneofthegreatestchallengesfacinghealthcareproviders.Thereisariskthatthequalityofhealthcareprovidedtoconsumerscanvarydependingontheskillsoftheprofessionalstoruneintothevaluesandpreferencesoftheindividualconsumer.Healthcareprovidersneedtolistentoandincorporateconsumers'experiencesintotheirhealthserviceofferingsinnewandcreativeways.Theyneedtoengageinadialoguewithvariousconsumergroupsusingmultiplechannels.Thereisaneedforanewbusinesslogic,whichwouldstructurallyhelptheproviders,onanindividualbasistodealwithmoresophisticatedhealthcareconsumers.Thisisareviewpaperandprovidesaframeworkandasetofstrategiesfordealingwithhealthconsumerdiversity.Wedrawonthreeinterrelatedtheories-opensystemstheory,servicemanagementresearchandtheconceptofconsumersegmentationtounderstandhealthcareconsumers'attitudesandbehaviour,andtheirexpectations.

简介：Thecross-fertilizationbetweenartificialintelligenceandcomputationalfinancehasresultedinsomeofthemostactiveresearchareasinfinancialengineering.Onedirectionistheapplicationofmachinelearningtechniquestopricingfinancialproducts,whichiscertainlyoneofthemostcomplexissuesinfinance.Intheliterature,whentheinterestrate,themeanrateofreturnandthevolatilityoftheunderlyingassetfollowgeneralstochasticprocesses,theexactsolutionisusuallynotavailable.Inthispaper,weshallillustratehowgeneticalgorithms(GAs),asanumericalapproach,canbepotentiallyhelpfulindealingwithpricing.Inparticular,wetesttheperformanceofbasicgeneticalgorithmsbyusingittothedeterminationofpricesofAsianoptions,whoseexactsolutionsisknownfromBlack-Scholesoptionpricingtheory.Thesolutionsfoundbybasicgeneticalgorithmsarecomparedwiththeexactsolution,andtheperformanceofGAsisewluatedaccordingly.Basedontheseewluations,somelimitationsofGAsinoptionpricingareexaminedandpossibleextensionstofutureworksarealsoproposed.

简介：Thispaperproposesasupervisedtraining-testmethodwithGeneticProgramming(GP)forpatternclassification.Comparedandcontrastedwithtraditionalmethodswithregardtodeterministicpatternclassifiers,thismethodistrueforbothlinearseparableproblemsandlinearnon-separableproblems.Forspecifictrainingsamples,itcanformulatetheexpressionofdiscriminatefunctionwellwithoutanypriorknowledge.Atlast,anexperimentisconducted,andtheresultrevealsthatthissystemiseffectiveandpractical.

简介：Optimisingbothqualitativeandquantitativefactorsisakeychallengeinsolvingconstructionfinancedecisions.Thesemi-structurednatureofconstructionfinanceoptimisationproblemsprecludesconventionaloptimisationtechniques.Withadesiretoimprovetheperformanceofthecanonicalgeneticalgorithm(CCA)whichischaracterisedbystaticcrossoverandmutationprobability,andtoprovidecontractorswithaprofit-risktrade-offcurveandcashflowprediction,anadaptivegeneticalgorithm(AGA)modelisdeveloped.TenprojectsbeingundertakenbyamajorconstructionfirminHongKongwereusedascasestudiestoevaluatetheperformanceofthegeneticalgorithm(CA).TheresultsofcasestudyrevealthattheACAoutperformedtheCGAbothintermsofitsqualityofsolutionsandthecomputationaltimerequiredforacertainlevelofaccuracy.TheresultsalsoindicatethatthereisapotentialforusingtheGAformodellingfinancialdecisionsshouldbothquantitativeandqualitativefactorsbeoptimisedsimultaneously.

简介：考虑到随机的操作费用在负担需求和水库水流入为热发电单位和无常弯，这份报纸的目的是概率地安排短期的热水的系统。因此，安排问题的随机的多客观的热水的产生在系统生产费用系数和系统负担与无常的明确的识别被提出，它被当作随机的变量。模糊方法论为解决为最好损害的答案包含目的和选择标准的复合的一个决策问题被利用了。有arithmetic-average-bound-blend转线路和小浪变化操作员的一个真实代码的基因算法被使用解决短期的可变头的热水的安排问题。起始的可行答案被实现随机的启发式的搜索获得了。搜索在操作产生限制以内被执行。每次间隔被介绍一个松热产生单位为考虑，满足需求在期间的平等限制每次间隔。而为完整的安排时期满足可得到的水的消费到它的最大程度的平等限制被介绍产生为特别时间间隔联合起来的松水疗院考虑。由松水疗院和松热产生单位的操作限制违背用外面的惩罚方法被小心。建议方法的有效性在二个样品系统上被表明。

简介：公司破产每年在损失花费了十亿美元到银行。因此承认风险预言是一个银行的贷款赞同决定过程的关键部分。为信用风险预言的传统的金融模型为describingtoday在金融健康和一个公司的潜在的破产之间的复杂关系不再是足够的。Inthis工作，一个多重分类器系统(在一个多重聪明的代理人系统嵌入)被建议预言一个公司的金融健康。在我们的模型，每个单个代理人(分类器)基于公司的仅仅偏爱的信息在信用风险的可能性上做预言。每代理人是一位专家，但是限制了知识(由特征代表了)关于公司。所有代理人的决定一起被联合形成最后的信用风险预言。实验证明我们的模型用benchmarkingCompustat美国公司数据集超过另外的存在方法。

简介：BybuildingupanAIDSmodelofChineseruralresidents,classifiedintofivegroupsbyincome,concludingsixcommodities(services),analysesthediversityofconsumptionbehavioroutofincomedifference.Wedrawthefollowingconclusions:Firstly,thereexistssomedifferencebetweendifferentincomegroupsincommodities(services)consumption.Secondly,increaseoffarmers'incomewillbefavorabletotheenhancementofconsumptionlevelandupgradingofconsumptionstructure.Thirdly,themediumincomegroup,asonespecialmass,featuresatransitionalmainbodyofconsumptionstructure.Finally,thepolicymakersshouldsupportthefarmerswithlowerincomeincompulsoryeducation,suchasmoreandnecessarytransferpayments.

简介：ThispaperaddressestheintegratedEarthobservationsatelliteschedulingproblem.Itisacomplicatedproblembecauseobservinganddownloadingoperationsarebothinvolved.Weuseanacyclicdirectedgraphmodeltodescribetheobservinganddownloadingintegratedschedulingproblem.Basedonthemodelwhichconsideringenergyconstraintsandstoragecapacityconstraints,wedevelopanefficientsolvingmethodusinganovelquantumgeneticalgorithm.Wedesignanewencodinganddecodingschemethatcangeneratefeasiblesolutionandincreasethediversityofthepopulation.TheresultsofthesimulationexperimentsshowthattheproposedmethodsolvestheintegratedEarthobservationsatelliteschedulingproblemwithgoodperformanceandoutperformsthegeneticalgorithmandgreedyalgorithmonallinstances.

简介：Inthispaper,aconstrainedgeneticalgorithm(CGA)isproposedtosolvethesinglemachinetotalweightedtardinessproblem.TheproposedCGAincorporatesdominancerulesfortheproblemunderconsiderationintotheGAoperators.ThisincorporationshouldenabletheproposedCGAtoobtainclosetooptimalsolutionswithmuchlessdeviationandmuchlesscomputationaleffortthantheconventionalGA(UGA).SeveralexperimentswereperformedtocomparethequalityofsolutionsobtainedbythethreeversionsofboththeCGAandtheUGAwiththeresultsobtainedbyadynamicprogrammingapproach.ThecomputationalresultsshowedthattheCGAwasbetterthantheUGAinbothqualityofsolutionsobtainedandtheCPUtimeneededtoobtaintheclosetooptimalsolutions.ThethreeversionsoftheCGAreducedthepercentagedeviationby15.6%,61.95%,and25%respectivelyandobtainedclosetooptimalsolutionswith59%lowerCPUtimethanwhatthethreeversionsoftheUGAdemanded.TheCGAperformedbetterthantheUGAintermsofqualityofsolutionsandcomputationaleffortwhenthepopulationsizeandthenumberofgenerationsaresmaller.