採用浮点数编码的遗传算法的问题

　　近期学习了一下遗传算法，于是參考了一篇论文。採用浮点数编码写了一个算法。执行了一下。公布收敛非常慢，遗传了几百代与几十代相比，仅仅是略显稳定，随机性比較强。并且easy限入局部极值中。比如。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