采用浮点数编码的遗传算法的问题

　　最近学习了一下遗传算法，于是参考了一篇论文，采用浮点数编码写了一个算法，运行了一下，发布收敛很慢，遗传了几百代与几十代相比，只是略显稳定，随机性比较强。而且容易限入局部极值中。例如，我以求函数xsin(10PI*x)+2.0为例，最大值大约在x=1.85附近，在x=1.65附近有一个局部极值。我写的算法大约70%的机会取得1.85附近的值，30%机会取得1.65附近的值。总之效果不理想。不知有没有朋友有兴趣帮我看一下。

　　初始化种群

        public List<PopUnit> InitPop()
        {
            if (CalculateFitnessHanler == null)
                throw new Exception("未设置计算表达式的事件CalculateHanler");
            if (m_Parameters.PopSize<=10)
                throw new Exception("种群数太小");

            List<PopUnit> pool = new List<PopUnit>();
             
            for (int i = 0; i <= m_Parameters.PopSize-1; i++)
            {
                double d = m_Random.NextDouble();
                d = m_Parameters.L + (m_Parameters.R - m_Parameters.L) * d;
                double val= CalculateFitnessHanler(new double[] { d });
                PopUnit unit = new PopUnit(d, val);
                pool.Add(unit);
            }
            return pool;
        }

　　选择操作

public List<PopUnit> Select(List<PopUnit> data)
        {
            int dropoutCount = (int)(m_Parameters.DropoutRate * data.Count);

            List<PopUnit> newpool = new List<PopUnit>();
            for (int i = 0; i <= dropoutCount - 1; i++)
            {
                newpool.Add(data[i].Clone() as PopUnit);
                newpool.Add(data[i].Clone() as PopUnit);
            }
            int tmpCount = data.Count - newpool.Count;
            for (int i = dropoutCount; i <= dropoutCount + tmpCount - 1; i++)
            {
                newpool.Add(data[i].Clone() as PopUnit);
            }
            return newpool;
        }

　　交叉操作：

 

public void CrossOver(double a,double b,out double a1,out double b1)
        {
            if (m_Random.NextDouble() > m_Parameters.CrossOverRate)
            {
                a1 = a;
                b1 = b;
                return;
            }
             

            double r = 0.1;
            double a2 = m_Random.NextDouble() * r;
            double b2 = m_Random.NextDouble() * r;

            a1 = (1 - a2) * a + b2 * b;
            b1 = (1 - b2) * b + a2 * a;
           
            
            if (a1 < m_Parameters.L)
                a1 = m_Parameters.L;
            if (b1 < m_Parameters.L)
                b1 = m_Parameters.L;
            if (a1 > m_Parameters.R)
                a1 = m_Parameters.R;
            if (b1 > m_Parameters.R)
                b1 = m_Parameters.R;
            //Console.WriteLine("crossover a1={0},b1={1}  ->  a2={2},b2={3}", a, b, a1, b1); 
        }

　　变异操作：

public double Mutation(double c)
        {
            double tmpd = m_Random.NextDouble();
            if (tmpd > m_Parameters.MutateRate)
            {
                return c;
            }
                        
            double k = 0.2;
            //k = (m_Parameters.MaxGenerations-T)/ (m_Parameters.MaxGenerations + 2D) * 0.4D;
            double res;
            if (m_Random.Next(2) == 1)
            {
                res = c + k * (m_Parameters.R - c) *m_Random.NextDouble();
            }
            else
            {
                res = c - k * (c - m_Parameters.L) *m_Random.NextDouble();
            }
            //Console.WriteLine("mutation: {0} -> {1}", c, res); 

            return res;
        }

 种群大小=100，交叉概率=0.7，变异概率=0.1