/*
* 0：EliteSelection算法
* 1：RankSelection算法
* 其他：RouletteWheelSelection 算法
*/
public static void bodyofgenetic()
{
trainFitnessFunction FitnessFunction = new trainFitnessFunction(Common.routelines);
int populationSize = 500; //种群最大规模
int selectionMethod = 0;
//适应度函数使用自定义的FitnessFunction，编码使用二进制编码，染色体长度为车次的的数目，每个世代个体数目为500，选择方式为“精英取舍”。
Population population = new Population(populationSize,
new BinaryChromosome(Common.routelines.Count()), FitnessFunction,
(selectionMethod == 0) ? (ISelectionMethod) new EliteSelection() :
(selectionMethod == 1) ? (ISelectionMethod) new RankSelection() :
(ISelectionMethod) new RouletteWheelSelection()
);
// iterations
int iter = 1;
int iterations = 500; //迭代最大周期
population.CrossoverRate = 0.9;
population.MutationRate = 0.03;
while (iter < iterations)
{
population.Crossover(); //交叉
population.Mutate(); //变异
population.RunEpoch(); //执行
iter++;
}
string resultbest = population.BestChromosome.ToString(); //最佳的染色体
// Common.services.Clear();
Phenotype bestResult = new Phenotype();
bestResult.init(resultbest); //解码，重新转化为车次链
double resulefit = bestResult.getFitness(); //最佳的适应度
}
//评价染色体，计算它的适应度,输出迭代的进度
public double Evaluate(IChromosome chromosome)
{
Common.services.Clear();
BinaryChromosome realDude = ((BinaryChromosome)chromosome);
string genome;//基因组
genome = realDude.Value.ToString();
Phenotype p = new Phenotype();
p.init(genome);
double fit = p.getFitness();
/*
* PROGRESS += 1 / GPROGRESS;
* evolutionProgress = PROGRESS * 100;
*/
return(1 / fit);
}