Esempio n. 1
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        private void FitnessSort(GENE[] a, int st, int ed)//适应度排序
        {
            if (st >= ed)
            {
                return;
            }
            int  i = st, j = ed;
            GENE tmp = a[i];

            while (i < j)
            {
                //while (i < j && CompareSmall(a[j].Fitness,tmp.Fitness))
                while (i < j && a[j].Fitness >= tmp.Fitness)
                {
                    j--;
                }
                if (i < j)
                {
                    a[i] = a[j];
                }
                //while (i < j && CompareSmall(tmp.Fitness,a[i].Fitness))
                while (i < j && a[i].Fitness < tmp.Fitness)
                {
                    i++;
                }
                if (i < j)
                {
                    a[j] = a[i];
                }
            }
            a[i] = tmp;
            FitnessSort(a, st, i - 1);
            FitnessSort(a, i + 1, ed);
        }
Esempio n. 2
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        private void Replicate()
        {
            GENE[] NextPopulation = new GENE[PopulationSize]; //声明下一代种群
            for (int i = 0; i < PopulationSize; i++)          //对种群中每个个体进行声明
            {
                NextPopulation[i] = new GENE(GENE.GeneLength);
            }

            double[] pi         = new double[PopulationSize];
            double   FitnessSum = 0;

            //复制
            FitnessSum = 0;
            FitnessSort(Population, 0, PopulationSize - 1);
            //采取“保留最佳精英策略”,将最优个体直接替代最差个体;
            //但最差个体仍有机会参与复制
            NextPopulation[0] = Population[PopulationSize - 1];
            //构造轮盘赌
            for (int i = 0; i < PopulationSize; i++)
            {
                FitnessSum += Population[i].Fitness;
            }

            pi[0] = Population[0].Fitness / FitnessSum;
            for (int i = 1; i < PopulationSize; i++)
            {
                pi[i] = Population[i].Fitness / FitnessSum + pi[i - 1];
            }

            Random rd = new Random();

            for (int i = 1; i < PopulationSize; i++)
            {
                double tmp  = rd.NextDouble();
                int    copy = 0;
                for (int j = 0; j < PopulationSize; j++)
                {
                    if (tmp <= pi[j])
                    {
                        copy = j; break;
                    }
                }
                NextPopulation[i] = Population[copy];
            }

            for (int i = 0; i < PopulationSize; i++)
            {
                Population[i] = NextPopulation[i];
            }

            //调试
            for (int i = 0; i < PopulationSize; i++)
            {
                Population[i].Fitness = FitnessFunc(Population[i]);
            }
        }
Esempio n. 3
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        private double FitnessFunc(GENE x)                 //适应度函数,index为染色体编号
        {
            var    result = network.Compute(x.GeneSerial); //计算对应函数值
            double tmp    = 0;

            for (int i = 0; i < result.Length; i++)
            {
                tmp += result[i];
            }
            return(tmp);
        }
Esempio n. 4
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        public GA_12(string saveNetFile, int inputDimension, int outputDimension)//默认构造函数
        {
            SaveNetFile     = saveNetFile;
            InputDimension  = inputDimension;
            OutputDimension = outputDimension;
            Good            = 0.8;

            PopulationSize = 50;
            MutationProb   = 0.001;
            GA_Iter_Max    = 1000;
            Population     = new GENE[PopulationSize];
            BestPerson     = new GENE[1000];
            for (int i = 0; i < PopulationSize; i++)
            {
                Population[i] = new GENE(GENE.GeneLength);
                BestPerson[i] = new GENE(GENE.GeneLength);
            }
        }
Esempio n. 5
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        public GA_12(int populationSize, double mutationProb, int ga_iter_Max, string saveNetFile,
                     int inputDimension, int outputDimension, double good)//构造函数重载
        {
            SaveNetFile     = saveNetFile;
            InputDimension  = inputDimension;
            OutputDimension = outputDimension;
            Good            = good;

            PopulationSize = populationSize;
            MutationProb   = mutationProb;
            GA_Iter_Max    = ga_iter_Max;
            //CrossProb = crossProb;
            Population = new GENE[PopulationSize];
            BestPerson = new GENE[1000];
            for (int i = 0; i < PopulationSize; i++)
            {
                Population[i] = new GENE(GENE.GeneLength);
                BestPerson[i] = new GENE(GENE.GeneLength);
            }
        }
Esempio n. 6
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        private double FitnessFunc(GENE x)//适应度函数
        {
            double tmp = 0;

            for (int i = 0; i < GENE.GeneLength; i++)
            {
                tmp += x.GeneSerial[i];
            }
            if (tmp > C)
            {
                return(0);
            }
            double tmp1  = 0;
            double alpha = 3.0;

            for (int i = 0; i < GENE.GeneLength; i++)
            {
                tmp1 += x.GeneSerial[i] * StockData[InveseIndex[i]][3];
            }


            double tmp2 = C;
            double beta = 3.0;

            for (int i = 0; i < GENE.GeneLength; i++)
            {
                tmp2 -= x.GeneSerial[i];
            }

            double tmp3 = 0;
            double gama = 1.0;

            for (int i = 0; i < GENE.GeneLength; i++)
            {
                tmp3 += x.GeneSerial[i] * abs(StockData[InveseIndex[i]][4] - EquityRatio) / EquityRatio;
            }

            return(alpha * tmp1 + beta * tmp2 - gama * tmp3);
        }
Esempio n. 7
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        public GA_3(double[][] stockData, int[] inveseIndex)
        {
            InveseIndex    = inveseIndex;
            C              = 100;
            StockData      = stockData;
            PopulationSize = 50;
            MutationProb   = 0.001;
            GA_Iter_Max    = 100;
            EquityRatio    = 50.0;

            BestAnswer = new GENE[1000];
            Population = new GENE[PopulationSize];

            for (int i = 0; i < PopulationSize; i++)
            {
                Population[i] = new GENE(GENE.GeneLength);
            }
            for (int i = 0; i < 1000; i++)
            {
                BestAnswer[i] = new GENE(GENE.GeneLength);
            }
        }
Esempio n. 8
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        public GA_3(double[][] stockData, int[] inveseIndex, double c, double equityRatio,
                    int populationSize, double mutationPro, int ga_iter_max)//构造函数重载
        {
            InveseIndex    = inveseIndex;
            C              = c;
            StockData      = stockData;
            PopulationSize = populationSize;
            MutationProb   = mutationPro;
            GA_Iter_Max    = ga_iter_max;
            EquityRatio    = equityRatio;

            Population = new GENE[PopulationSize];
            BestAnswer = new GENE[1000];

            for (int i = 0; i < PopulationSize; i++)
            {
                Population[i] = new GENE(GENE.GeneLength);
            }
            for (int i = 0; i < 1000; i++)
            {
                BestAnswer[i] = new GENE(GENE.GeneLength);
            }
        }