Beispiel #1
0
        /// <summary>
        /// Calculates the adjusted fitness of all genomes.
        /// Also syncs up species fitness information.
        /// </summary>
        public void AdjustFitness()
        {
            Fitness.TotalAdjustedScore = 0.0;

            foreach (var genome in Genomes)
            {
                GenomeFitnessInformation genomeFitness = genome.Fitness;
                genomeFitness.AdjustedScore = genomeFitness.Score;

                if ((Age - AgePrime + 1 - Const.SpeciesStagnationAge >= 0) || OnDeathBed)
                {
                    genomeFitness.AdjustedScore *= 0.01;
                }
                if (Age <= Const.AgeOfYouthThresh)
                {
                    genomeFitness.AdjustedScore *= Const.YouthRewardCoefficient;
                }
                if (genomeFitness.AdjustedScore < 0)
                {
                    genomeFitness.AdjustedScore = 0;
                }

                genomeFitness.AdjustedScore /= Size();

                Fitness.TotalAdjustedScore += genomeFitness.AdjustedScore;
            }

            Fitness.AverageAdjustedScore = Fitness.TotalAdjustedScore / Size();
        }
Beispiel #2
0
 public static void Pole2_Evaluate(Random rando, Network network, GenomeFitnessInformation fitnessInfo)
 {
 }
Beispiel #3
0
        public static void Pole1_Evaluate(Random rando, Network network, GenomeFitnessInformation fitnessInfo)
        {
            int maxSteps = 100000;
            int steps    = 0;
            int y;

            Dictionary <int, double> inputArray = new Dictionary <int, double>()
            {
                { 1, 0.0 },
                { 2, 0.0 },
                { 3, 0.0 },
                { 4, 0.0 },
            };

            CartInfo cartInfo = new CartInfo(rando);

            if (false)
            {
                inputArray[1] = (cartInfo.x + 2.4) / 4.8;;
                inputArray[2] = (cartInfo.x_Dot + .75) / 1.5;
                inputArray[3] = (cartInfo.theta + twelve_degrees) / .41;
                inputArray[4] = (cartInfo.theta_Dot + 1.0) / 2.0;
                network.LoadSensors(inputArray);
                network.Initialize();
            }

            while (steps++ < maxSteps)
            {
                inputArray[1] = (cartInfo.x + 2.4) / 4.8;
                inputArray[2] = (cartInfo.x_Dot + .75) / 1.5;
                inputArray[3] = (cartInfo.theta + twelve_degrees) / .41;
                inputArray[4] = (cartInfo.theta_Dot + 1.0) / 2.0;
                network.LoadSensors(inputArray);

                Dictionary <int, double> outputArray = network.Activate();

                double val5 = 0.0;
                double val6 = 0.0;

                if (outputArray.Keys.Contains(5))
                {
                    val5 = outputArray[5];
                }
                if (outputArray.Keys.Contains(6))
                {
                    val6 = outputArray[6];
                }

                if (val5 > val6)
                {
                    y = 0;
                }
                else
                {
                    y = 1;
                }

                cartInfo.AdjustCart(y);

                if (!cartInfo.IsStable())
                {
                    break;
                }
            }

            fitnessInfo.Score = steps;
        }