gaot

代码说明：

  tep 1：对遗传算法的运行参数进行赋值。参数包括种群规模、变量个数、交叉概率、变异概 率以及遗传运算的终止进化代数。 Step 2：建立区域描述器。根据轨道交通与常规公交运营协调模型的求解变量的约束条件，设 置变量的取值范围。 Step 3：在Step 2的变量取值范围内，随机产生初始群体，代入适应度函数计算其适应度值。 Step 4：执行比例选择算子进行选择操作。 Step 5：按交叉概率对交叉算子执行交叉操作。 Step 6：按变异概率执行离散变异操作。 Step 7：计算Step 6得到局部最优解中每个个体的适应值，并执行最优个体保存策略。 Step 8：判断是否满足遗传运算的终止进化代数，不满足则返回Step 4，满足则输出运算结果。 其次，运用遗传算法工具箱。(Matlab genetic algorithm toolbox)

文件列表：

gaot\gaot\adjswapMutation.m
gaot\gaot\arithXover.m
gaot\gaot\b2f.m
gaot\gaot\binaryExample.m
gaot\gaot\binaryMutation.m
gaot\gaot\boundaryMutation.m
gaot\gaot\calcbits.m
gaot\gaot\Contents.m
gaot\gaot\coranaEval.m
gaot\gaot\coranaFeval.m
gaot\gaot\coranaMin.m
gaot\gaot\cyclicXover.m
gaot\gaot\delta.m
gaot\gaot\dists.m
gaot\gaot\EER.m
gaot\gaot\enhancederXover.m
gaot\gaot\erXover.m
gaot\gaot\f2b.m
gaot\gaot\floatExample.m
gaot\gaot\floatGradExample.m
gaot\gaot\ga.m
gaot\gaot\gademo.m
gaot\gaot\gademo1.m
gaot\gaot\gademo1eval1.m
gaot\gaot\gademo2.m
gaot\gaot\gademo3.m
gaot\gaot\gaMichEval.m
gaot\gaot\gaotv5.ps
gaot\gaot\gaZBGrad.m
gaot\gaot\gaZBGradEval.m
gaot\gaot\heuristicXover.m
gaot\gaot\initializega.m
gaot\gaot\initializeoga.m
gaot\gaot\inversionMutation.m
gaot\gaot\linerorderXover.m
gaot\gaot\maxGenTerm.m
gaot\gaot\multiNonUnifMutation.m
gaot\gaot\nonUnifMutation.m
gaot\gaot\normGeomSelect.m
gaot\gaot\optMaxGenTerm.m
gaot\gaot\orderBasedExample.m
gaot\gaot\orderbasedXover.m
gaot\gaot\parse.m
gaot\gaot\partmapXover.m
gaot\gaot\plotCorana.m
gaot\gaot\README
gaot\gaot\roulette.m
gaot\gaot\shiftMutation.m
gaot\gaot\simpleXover.m
gaot\gaot\singleptXover.m
gaot\gaot\startup.m
gaot\gaot\swapMutation.m
gaot\gaot\threeswapMutation.m
gaot\gaot\tournSelect.m
gaot\gaot\tspEval.m
gaot\gaot\unifMutation.m
gaot\gaot\uniformXover.m
gaot\gaot
gaot