/// <summary>
/// 概率进行个体的变异
/// </summary>
private void Mutation()
{
//概率执行变异
ProbabilityExcute.Excute(mutationRate, delegate()
{
if (nextGenomeList.Count <= 1)
{
return;
}
nextGenomeList[ProbabilityExcute.globleRand.Next(1, nextGenomeList.Count)].Mutate();
});
}
/// <summary>
/// 繁衍子代
/// </summary>
private void Breed()
{
//概率执行繁衍
ProbabilityExcute.Excute(crossoverRate, delegate()
{
//随机两个父代
Genome parent1 = GenomeList[ProbabilityExcute.globleRand.Next(0, GenomeList.Count / 2)];
Genome parent2 = GenomeList[ProbabilityExcute.globleRand.Next(0, GenomeList.Count / 2)];
//生成子代,并将子代填充到容器中
Genome[] children = parent1.Crossover(parent2);
foreach (Genome child in children)
{
nextGenomeList.Add(child);
}
});
}
/// <summary>
/// 创建基因用的函数
/// </summary>
/// <param name="count"></param>
/// <returns></returns>
private Point[] createGensHandle(int count)
{
List <Point> returnList = new List <Point>();
Point point = Conf.NullPoint;
for (int i = 0; i < count; i++)
{
while (true)
{
point = ProbabilityExcute.CreateRandGene();
//如果随机的棋子为空,没有重复生成点坐标,并周围1个单元格内有其他棋子.则停止循环
if (!returnList.Contains(point) && Chess.Find(point).Type == ChessType.NULL && checkPoint(point, 2))
{
returnList.Add(point);
break;
}
}
}
return(returnList.ToArray());
}
/// <summary>
/// 生成选择遗传的概率,并采用"轮盘赌"模型执行选择操作
/// 分配更大的"可能被复制"给适应性更高的个体
/// </summary>
private void Selection()
{
//更新整个种群的适应度和
UpdateTotalFitness();
//创建下一代种群容器
nextGenomeList = new List <Genome>();
////最优个体保存原则
//nextGenomeList.Add(GenomeList[GenomeList.Count - 1]);
//更新每个个体被选中的概率---排除个体最优
for (int i = 0; i < GenomeList.Count; i++)
{
//概率执行复制操作
ProbabilityExcute.Excute((Math.Abs(GenomeList[i].Fitness) / this.totalFitness), delegate()
{
//复制到下一代
nextGenomeList.Add(GenomeList[i]);
});
}
}
