├── .DS_Store ├── README.md ├── README └── Awards.jpg ├── 免疫优化算法在物流配送中心选址中的应用 ├── Cross.m ├── IAdata.mat ├── Mutation.m ├── Select.m ├── bestselect.m ├── centre.fig ├── concentration.m ├── draw.m ├── excellence.m ├── figure.fig ├── fitness.m ├── incorporate.m ├── main.m ├── popinit.m ├── similar.m └── test.m ├── 基于PSO工具箱的函数寻优算法 ├── .DS_Store ├── PSOt │ ├── forcecol.m │ ├── forcerow.m │ ├── goplotpso.m │ ├── linear_dyn.m │ ├── normmat.m │ ├── pso_Trelea_vectorized.m │ └── spiral_dyn.m └── testfunctions │ ├── DeJong_f2.m │ ├── DeJong_f3.m │ ├── DeJong_f4.m │ ├── Foxhole.m │ ├── Griewank.m │ ├── NDparabola.m │ ├── Rastrigin.m │ ├── Rosenbrock.m │ ├── ackley.m │ ├── alpine.m │ ├── f6.m │ ├── f6_bubbles_dyn.m │ ├── f6_linear_dyn.m │ ├── f6_spiral_dyn.m │ ├── f6mod.m │ ├── forcecol.m │ ├── forcerow.m │ ├── goplotpso.m │ ├── linear_dyn.m │ ├── main.m │ ├── normmat.m │ ├── pso_Trelea_vectorized.m │ ├── spiral_dyn.m │ ├── test_func.m │ └── tripod.m ├── 基于动态粒子群算法的动态环境寻优算法 ├── DF1function.m ├── fitnessRecord.mat ├── main.m └── result.mat ├── 基于粒子群算法的PID控制器优化设计 ├── GA_run.m ├── PID_Model.mdl ├── PSO.m ├── PSO_PID.m └── 问题解决思路.pdf ├── 基于蚁群算法的三位路径规划算法 ├── CacuFit.m ├── CacuQfz.m ├── HeightData.mat ├── czfz.m ├── data.m ├── data1.m ├── main.m └── searchpath.m ├── 基于遗传算法和非线性规划的函数寻优算法 ├── 案例1 │ ├── Code.m │ ├── Cross.m │ ├── Genetic.m │ ├── Mutation.m │ ├── Select.m │ ├── fun.m │ └── test.m ├── 案例1非线性 │ ├── Code.m │ ├── Cross.m │ ├── Mutation.m │ ├── Select.m │ ├── fun.m │ ├── main.m │ ├── nonlinear.m │ └── test.m ├── 案例2 │ ├── Code.m │ ├── Cross.m │ ├── Genetic.m │ ├── Mutation.m │ ├── Select.m │ ├── fun.m │ └── test.m ├── 案例2非线性 │ ├── Code.m │ ├── Cross.m │ ├── Genetic.m │ ├── Mutation.m │ ├── Select.m │ ├── fun.m │ ├── nonlinear.m │ └── test.m ├── 案例3 │ ├── Code.m │ ├── Cross.m │ ├── Genetic.m │ ├── Mutation.m │ ├── Select.m │ ├── fun.m │ └── test.m └── 案例3非线性 │ ├── Code.m │ ├── Cross.m │ ├── Genetic.m │ ├── Mutation.m │ ├── Select.m │ ├── fun.m │ ├── nonlinear.m │ └── test.m ├── 基于遗传算法的BP神经网络优化算法 ├── R2009a │ ├── Bpfun.m │ ├── GABPMain.m │ ├── Objfun.m │ ├── callbackfun.m │ └── data.mat └── R2014a │ ├── Bpfun.m │ ├── GABPMain.m │ ├── Objfun.m │ ├── callbackfun.m │ └── data.mat ├── 基于遗传算法的LQR控制器优化设计 ├── Active_Suspension_LQR.mdl ├── GA_LQR.m └── GA_LQR_run.m ├── 基于遗传算法的TSP算法 ├── CityPosition1.mat ├── CityPosition2.mat ├── CityPosition3.mat ├── Distanse.m ├── DrawPath.m ├── Fitness.m ├── GA_TSP.m ├── InitPop.m ├── Mutate.m ├── OutputPath.m ├── PathLength.m ├── Recombin.m ├── Reins.m ├── Reverse.m ├── Select.m ├── Sus.m └── dsxy2figxy.m ├── 多种群遗传算法的函数优化算法 ├── EliteInduvidual.m ├── MPGA.m ├── ObjectFunction.m ├── SGA.m └── immigrant.m ├── 支持向量机分类——基于乳腺组织电阻抗特性的乳腺癌诊断 ├── BreastTissue_data.mat ├── libsvm-mat-2[1].89-3[FarutoUltimate3.0Mcode] │ ├── COPYRIGHT │ ├── Makefile │ ├── README │ ├── heart_scale.mat │ ├── implement[by faruto] │ │ ├── SVC.m │ │ ├── SVC_test.m │ │ ├── SVMcgForClass.m │ │ ├── SVMcgForRegress.m │ │ ├── SVR.m │ │ ├── SVR_test.m │ │ ├── TutorialForFarutoUltimate3.0.pdf │ │ ├── TutorialTest.m │ │ ├── VF.m │ │ ├── a_template_flow_usingSVM_class.m │ │ ├── a_template_flow_usingSVM_regress.m │ │ ├── fasticaForSVM.m │ │ ├── gaSVMcgForClass.m │ │ ├── gaSVMcgForRegress.m │ │ ├── gaSVMcgpForRegress.m │ │ ├── myprivate │ │ │ ├── gatbx[Sheffield] │ │ │ │ ├── bs2rv.m │ │ │ │ ├── contents.m │ │ │ │ ├── crtbase.m │ │ │ │ ├── crtbp.m │ │ │ │ ├── crtrp.m │ │ │ │ ├── migrate.m │ │ │ │ ├── mpga.m │ │ │ │ ├── mut.m │ │ │ │ ├── mutate.m │ │ │ │ ├── mutbga.m │ │ │ │ ├── ranking.m │ │ │ │ ├── recdis.m │ │ │ │ ├── recint.m │ │ │ │ ├── reclin.m │ │ │ │ ├── recmut.m │ │ │ │ ├── recombin.m │ │ │ │ ├── reins.m │ │ │ │ ├── rep.m │ │ │ │ ├── resplot.m │ │ │ │ ├── rws.m │ │ │ │ ├── scaling.m │ │ │ │ ├── select.m │ │ │ │ ├── sus.m │ │ │ │ ├── xovdp.m │ │ │ │ ├── xovdprs.m │ │ │ │ ├── xovmp.m │ │ │ │ ├── xovsh.m │ │ │ │ ├── xovshrs.m │ │ │ │ ├── xovsp.m │ │ │ │ └── xovsprs.m │ │ │ ├── plotroc.m │ │ │ └── roc.m │ │ ├── pcaForSVM.m │ │ ├── psoSVMcgForClass.m │ │ ├── psoSVMcgForRegress.m │ │ ├── readme[by faruto].txt │ │ ├── scaleForSVM.m │ │ ├── svmplot.m │ │ └── test_for_ica_SVM.m │ ├── libsvm 参数说明.txt │ ├── libsvmread.c │ ├── libsvmread.mexw32 │ ├── libsvmwrite.c │ ├── libsvmwrite.mexw32 │ ├── make.m │ ├── readme[by faruto].txt │ ├── svm.cpp │ ├── svm.h │ ├── svm.obj │ ├── svm_model_matlab.c │ ├── svm_model_matlab.h │ ├── svm_model_matlab.obj │ ├── svmpredict.c │ ├── svmpredict.mexw32 │ ├── svmtrain.c │ ├── svmtrain.mexw32 │ ├── test_data │ │ ├── wine_test.mat │ │ └── x123.mat │ └── 安装说明.txt └── main.m ├── 支持向量机的回归拟合——混凝土抗压强度预测 ├── concrete_data.mat └── main.m ├── 有监督学习神经网络的回归拟合——基于红外光谱的汽油辛烷值预测 ├── main_2009a.m ├── main_2014a.m └── spectra_data.mat ├── 粒子群算法的寻优算法 ├── sample1 │ ├── MexicoHatnew.m │ ├── PSO0.m │ ├── PSO1.m │ ├── PSO2.m │ ├── PSO3.m │ ├── PSO4.m │ ├── fun.m │ ├── main.m │ └── wchange.m ├── sample2-Rastrgrin │ ├── PSO.m │ ├── fun.m │ ├── pso.fig │ ├── pso.mat │ ├── rastrigrin.fig │ └── rastrigrin.m └── 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