コード例 #1
0
        static void Main(string[] args)
        {
            var population       = new Population(6);
            var geneticAlgorythm = new GeneticAlgorithm(population, FitnessFunction, -3, 1, 0, 3, 1);

            geneticAlgorythm.Execute();
        }
コード例 #2
0
        static void Main(string[] args)
        {
            //TestFunc1
            double[] lowerBd      = new double[] { -100.0, -100.0, -100.0, -100.0, -100.0, -100.0, -100.0, -100.0, -100.0, -100.0 };
            double[] upperBd      = new double[] { 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0 };
            int[]    decimalPlace = new int[] { 15, 15, 15, 15, 15, 15, 15, 15, 15, 15 };//[0,15]

            //double[] lowerBd = new double[] {-Math.PI,-Math.PI };
            //double[] upperBd = new double[] { Math.PI, Math.PI };
            //int[] decimalPlace = new int[] { 15, 15};//[0,15]

            ////TestFunc5
            //double[] lowerBd = new double[] { -5, -5 };
            //double[] upperBd = new double[] { 5, 5 };
            //int[] decimalPlace = new int[] { 15, 15 };//[0,15]


            int maxIteration = 1000;// maximum iteration


            int    numPopulation  = 30;                  // number of Population
            int    ChromosomeSize = lowerBd.Length;      //num of the parameters
            int    PS             = ChromosomeSize * 10; //populationSize
            double PcUpper        = 0.95;                //crossover upper Probability
            double PcLower        = 0.7;                 //crossover lower Probability
            double PmUpper        = 0.1;                 //mutation upper Probability
            double PmLower        = 0.01;                //mutation lower Probability

            List <int>    populationSizeList       = new List <int>(numPopulation);
            List <double> crossoverProbabilityList = new List <double>(numPopulation);
            List <double> mutationProbabilityList  = new List <double>(numPopulation);

            for (int i = 0; i < numPopulation; i++)
            {
                populationSizeList.Add(PS);
                crossoverProbabilityList.Add(myRandom.NextDouble(PcLower, PcUpper));
                mutationProbabilityList.Add(myRandom.NextDouble(PmLower, PmUpper));
            }

            int[]    populationSize       = populationSizeList.ToArray();
            double[] crossoverProbability = crossoverProbabilityList.ToArray();
            double[] mutationProbability  = mutationProbabilityList.ToArray();


            int currentIteration     = 0;
            GeneticAlgorithm Genetic = new GeneticAlgorithm(ChromosomeSize, lowerBd, upperBd, decimalPlace,
                                                            populationSize, crossoverProbability, mutationProbability,
                                                            numPopulation, currentIteration, maxIteration);

            Genetic.PopsOfGaIni();
            Genetic.Execute();
            Console.WriteLine("end~~~");
            Console.ReadKey();
        }