简介：Toresearchtheeffectoftheselectionmethodofmulti-objectsgeneticalgorithmproblemonoptimizingresult,thismethodisanalyzedtheoreticallyanddiscussedbyusinganautonomousunderwatervehicle(AUV)asanobject.Achangingweightvtluemethodisputforwardandaselectionformulaismodified.SomeexperimentswereimplementedonanAUV.TwinBurger.Theresultsshowsthatthismethodiseffectiveandfeasible.

简介：Itisverydifficulttoestimateexactvaluesoftimeandcostofanactivityinprojectschedulingprocessbecausemanyuncertainfactors,suchasweather,productivitylevel,humanfactorsetc.,dynamicallyaffectthemduringprojectimplementationprocess.AGAs-basedfullyfuzzyoptimaltime-costtrade-offmodelispresentedbasedonfuzzysetsandgeneticalgorithms(GAs).Intihsmodelallparametersandvariablesarecharacteristicsbyfuzzynumbers.AndthenGAsisadoptedtosearchfortheoptimalsolutiontothismodel.Themethodsolvesthetime-costtrade-offproblemsunderanuncertainenvironmentandisprovedpracticablethroughagivingexampleinshipbuildingscheduling.

简介：影响的因素在水下车辆声纳自我噪音被分析，并且基因算法和背繁殖(BP)神经网络被联合预言在水下车辆声纳自我噪音。试验性的结果表明那在水下车辆sonarself噪音能被基于的一个GA-BP神经网络精确地预言实际在水下车辆声纳数据。