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：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.