Exemplo n.º 1
0
 // Copies all Elements from population to oldPopulation
 public void clone()
 {
     for (int i = 0; i < populationSize; i++)
     {
         oldPopulation[i] = new Population(population[i].Path, population[i].Genome, population[i].Rating, population[i].Mutated, population[i].Selected);
     }
 }
Exemplo n.º 2
0
        // This function uses genomePart Uniform Crossover. It works like a crossover function but uses a significantly shorter mask genome
        // because it always chooses an entire 8bit block
        public void eightBitCrossover()
        {
            // Size of current new generation is 40% of old generation (before crossover)
            int selectionSize = Convert.ToInt32(0.4 * populationSize);
            int r;
            Genome parent1 = new Genome();
            Genome parent2 = new Genome();
            bool[] mask;
            Genome child1 = new Genome();
            Genome child2 = new Genome();

            // Iterates from selection Size (current maximum of the new population) to population size, the wanted maximum. That means 60% come from crossover
            for (int i = selectionSize; i < populationSize; i++)
            {
                // Randomly selects first parent
                r = Variables.getRandomInt(0, selectionSize);
                parent1 = oldPopulation[r].Genome;

                // Randomly selects second parent
                r = Variables.getRandomInt(0, selectionSize);
                parent2 = oldPopulation[r].Genome;

                // Crates a random mask genome in form of a char[]. char[] is much faster than bool[], genome or bitarray and for our purpose the format doesn't matter
                mask = GenomePart.getRandomGenome();

                for (int j = 0; j < 20; j++)
                {
                    // Checks the mask
                    if (mask[j])
                    {
                        // If mask == 1 then the i'th 8bit block will be selected from parent1 for child1 and from parent2 for child2
                        // k = j*8 since every position in the mask genome covers 8 positions in the actual genome
                        for (int k = j * 8; k < j * 8 + 8; k++)
                        {
                            child1.Genome1.Set(k, parent1.Genome1.Get(k));
                            child2.Genome1.Set(k, parent2.Genome1.Get(k));
                        }
                    }
                    else
                    {
                        // If mask == 0 then the i'th 8bit block will be selected from parent1 for child2 and from parent2 for child1
                        // k = j*8 since every position in the mask genome covers 8 positions in the actual genome
                        for (int k = j * 8; k < j * 8 + 8; k++)
                        {
                            child1.Genome1.Set(k, parent2.Genome1.Get(k));
                            child2.Genome1.Set(k, parent1.Genome1.Get(k));
                        }
                    }
                }

                Genome.genomeToPath(child1);
                population[i] = new Population(Variables.path, child1, 0, false, false);

                // Counts up since we are adding 2 children per iteration, not just one
                i++;

                // If we are not yet at maximum (which could happen due to double->int conversion) we add the second child too
                if (i < populationSize)
                {
                    Genome.genomeToPath(child2);
                    population[i] = new Population(Variables.path, child2, 0, false, false);
                }
            }
        }
Exemplo n.º 3
0
        // This function selects via tournament selection. Selected paths are not removed from the population, so the same path can be chosen several times
        public void tournamentSelection()
        {
            int highestFitness = 0, r = 0, tournamentSize = 2;

            // New Population consists of 40% Selection
            int selectionSize = Convert.ToInt32(0.4 * populationSize);

            // Iterates over the new population
            for (int j = 0; j < selectionSize; j++)
            {
                // Chooses tournamentSize members from the old population and saves the best one to highestFitness
                for (int i = 0; i < tournamentSize; i++)
                {
                    r = Variables.getRandomInt(0, selectionSize);

                    if (i == 0 || oldPopulation[r].Rating > oldPopulation[highestFitness].Rating)
                        highestFitness = r;
                }

                // Adds the winner of the tournament (highestFitness) to the new population
                population[j] = new Population(oldPopulation[highestFitness].Path, oldPopulation[highestFitness].Genome, oldPopulation[highestFitness].Rating, false, true);
            }
        }
Exemplo n.º 4
0
        // This function uses two fixed points for two-point crossover. For better performance these two points may be made random and/or adjusted over time
        // For example, in higher generations they could be moved further to the back
        public void twoFixedPointCrossover()
        {
            int selectionSize = Convert.ToInt32(0.4 * populationSize);
            Genome parent1 = new Genome();
            Genome parent2 = new Genome();
            Genome child1 = new Genome();
            Genome child2 = new Genome();
            int r;
            int fixpoint1 = 4;
            int fixpoint2 = 15;

            for (int i = selectionSize; i < populationSize; i++)
            {
                // Randomly selects first parent
                r = Variables.getRandomInt(0, selectionSize);
                parent1 = oldPopulation[r].Genome;

                // Randomly selects second parent
                r = Variables.getRandomInt(0, selectionSize);
                parent2 = oldPopulation[r].Genome;

                for (int j = 0; j < 20; j++)
                {
                    // Case 1: fixpoint 1 to fixpoint 2, fixpoints exclusive
                    if (j > fixpoint1 && j < fixpoint2)
                    {
                        // Iterates over the eight-bit-blocks and adds all bits to the respective parent
                        for (int k = j * 8; k < j * 8 + 8; k++)
                        {
                            child1.Genome1.Set(k, parent1.Genome1.Get(k));
                            child2.Genome1.Set(k, parent2.Genome1.Get(k));
                        }
                    }
                    // Case 2: Before fixpoint 1 and after fixpoint 2, fixpoints inclusive
                    else
                    {
                        // Iterates over the eight-bit-blocks and adds all bits to the respective parent
                        for (int k = j * 8; k < j * 8 + 8; k++)
                        {
                            child1.Genome1.Set(k, parent2.Genome1.Get(k));
                            child2.Genome1.Set(k, parent1.Genome1.Get(k));
                        }
                    }
                }

                Genome.genomeToPath(child1);
                population[i] = new Population(Variables.path, child1, 0, false, false);

                // Counts up since we are adding 2 children per iteration, not just one
                i++;

                // If we are not yet at maximum (which could happen due to double->int conversion) we add the second child too
                if (i < populationSize)
                {
                    Genome.genomeToPath(child2);
                    population[i] = new Population(Variables.path, child2, 0, false, false);
                }
            }
        }
Exemplo n.º 5
0
        // This function uses single-bit Uniform Crossover.
        public void singleBitCrossover()
        {
            // Size of current new generation is 40% of old generation (before crossover)
            int selectionSize = Convert.ToInt32(0.4 * populationSize);
            int r;
            Genome parent1 = new Genome();
            Genome parent2 = new Genome();
            Genome mask = new Genome();
            Genome child1 = new Genome();
            Genome child2 = new Genome();

            // Iterates from selection Size (current maximum of the new population) to population size, the wanted maximum. That means 60% come from crossover
            for (int i = selectionSize; i < populationSize; i ++)
            {
                // Randomly selects first parent
                r = Variables.getRandomInt(0,selectionSize);
                parent1 = oldPopulation[r].Genome;

                // Randomly selects second parent
                r = Variables.getRandomInt(0, selectionSize);
                parent2 = oldPopulation[r].Genome;

                // Crates a random mask genome. This function sucks.
                mask = new Genome(true);

                // Generates children
                for (int j = 0; j < 160; j++)
                {
                    if (mask.Genome1.Get(j))
                    {
                        // If mask(i) is 1 then child1 gets the value at that point from parent1, child2 from parent2
                        child1.Genome1.Set(j, parent1.Genome1.Get(j));
                        child2.Genome1.Set(j, parent2.Genome1.Get(j));
                    }
                    else
                    {
                        // If mask(i) is 0 then child1 gets the value at that point from parent2, child2 from parent1
                        child1.Genome1.Set(j, parent2.Genome1.Get(j));
                        child2.Genome1.Set(j, parent1.Genome1.Get(j));
                    }
                }

                // Adds the genome and its corresponding path to the population at position i. Also sets Selected to false.
                Genome.genomeToPath(child1);
                population[i] = new Population(Variables.path, child1, 0, false, false);

                // Counts up since we are adding 2 children per iteration, not just one
                i++;

                // If we are not yet at maximum (which could happen due to double->int conversion) we add the second child too
                if (i < populationSize)
                {
                    Genome.genomeToPath(child2);
                    population[i] = new Population(Variables.path, child2, 0, false, false);
                }
            }
        }
Exemplo n.º 6
0
        // This function is a straight selection which simply seletc the x best members of the population. Members cannot be chosen several times
        public void selection()
        {
            // New Population consists of 40% Selection
            int selectionSize = Convert.ToInt32(0.4 * populationSize);
            int d;

            // Iterates over the new population until selectionSize is met
            for (int i = 0; i < selectionSize; i++)
            {
                // Stores the highest rating
                d = 0;

                // Iterates over the old population, looking for the highest rating
                for (int j = 0; j < populationSize; j++)
                {
                    if (oldPopulation[j].Rating > oldPopulation[d].Rating)
                        d = j;
                }

                // Takes the path with the highest rating from old population and sets Selected/Mutated
                population[i] = new Population(oldPopulation[d].Path, oldPopulation[d].Genome, oldPopulation[d].Rating, false, true);

                // Sets the Rating of the selected path to 0 so it is not selected again
                oldPopulation[d].Rating = 0;
            }
        }
Exemplo n.º 7
0
        // This selection function uses a wighted (roulette wheel) selection process where members with a high fitness have a higher chance to be chosen.
        // Members can be chosen repeatedly
        // This function is equivalent to rouletteWheelSelection in functionality but not in implementation. The other function should be prefered unless it produces problems.
        public void rouletteWheelSelection2()
        {
            double totalFitness = 0;

            // New Population consists of 40% Selection
            int selectionSize = Convert.ToInt32(0.4 * populationSize);

            // Calculates toal Fitness of the old population
            for (int i = 0; i < populationSize; i++)
            {
                totalFitness += oldPopulation[i].Rating;
            }

            // Normalizes all fitness values
            for (int i = 0; i < populationSize; i++)
            {
                oldPopulation[i].Rating = oldPopulation[i].Rating / totalFitness;
            }

            Population[] tempPopulation = new Population[populationSize];
            double j;
            int d;

            // Sorts oldPopulation into temppOpulation by descending Fitness Rating
            for (int l = 0; l < populationSize; l++)
            {
                j = 0;
                d = 0;

                // Finds the member in oldPopulation with the highest rating
                for (int i = 0; i < populationSize; i++)
                {
                    if (oldPopulation[i].Rating > j)
                    {
                        j = oldPopulation[i].Rating;
                        d = i;
                    }
                }

                // oldPopulation[d], which is the member with the highest rating, goes to position i
                tempPopulation[l] = new Population(oldPopulation[d].Path, oldPopulation[d].Genome, oldPopulation[d].Rating, false, true);

                // Sets the current oldPopulation Rating to 0 so it is not selected again
                oldPopulation[d].Rating = 0;
            }

            double acc = 0;

            // Calculates the accumulated rating for every position so every member has it's own Rating + all previous ones as it's rating
            for (int i = 0; i < populationSize; i++)
            {
                acc += tempPopulation[i].Rating;
                tempPopulation[i].Rating = acc;
            }

            double r;

            for (int i = 0; i < selectionSize; i++)
            {
                r = Variables.getRandomNumber(0, 1);
                d = 0;

                while (tempPopulation[d].Rating <= r)
                    d++;

                population[i] = new Population(tempPopulation[d].Path, tempPopulation[d].Genome, tempPopulation[d].Rating, false, true);
            }
        }
Exemplo n.º 8
0
        // This selection function uses a weighted (roulette wheel) selection process where members with a high fitness have a higher chance to be chosen.
        // Members can be chosen repeatedly
        public void rouletteWheelSelection()
        {
            // Sums up all ratings to a total
            double totalFitness = 0;
            double r;
            double c;
            int j;

            // Calculates toal Fitness of the old population
            for (int i = 0; i < populationSize; i++)
            {
                totalFitness += oldPopulation[i].Rating;
            }

            // New Population consists of 40% Selection
            int selectionSize = Convert.ToInt32(0.4 * populationSize);

            // Creates <selectionSize> new members for the new population
            for (int i = 0; i < selectionSize; i++)
            {
                // Random Number
                r = Variables.getRandomNumber(0, totalFitness);

                // Current Sum and iterator are 0
                c = 0;
                j = 0;

                // Sums over oldPopulation until the sum is bigger than the random number
                do
                {
                        c += oldPopulation[j].Rating;
                        j++;
                } while (c < r);

                // Selects j - 1 (since we did j++ after fulfilling the condition) from the oldPopulation and adds it to the new one at i
                population[i] = new Population(oldPopulation[j - 1].Path, oldPopulation[j - 1].Genome, oldPopulation[j - 1].Rating, false, true);
            }
        }
Exemplo n.º 9
0
        // This function is only used once (Generation 0) and populates the algorithm with randomly generated paths.
        public void initialize()
        {
            double rating;

            // Generates an initial population of size <PopulationSize> along with their fitness
            for (int i = 0; i < populationSize; i++)
            {
                // Generates a random path of length 20 and saves it to the global path
                EZPathFollowing.PathPrimitives.generatePath();

                // Creates a Simulation for this path
                Variables.simulation = new Simulation(Variables.vehicle, Variables.path);
                Variables.simulation.run();

                // Rates the Path and adds the rating to the ratings List
                rating = FitnessFunction.fitness(Variables.simulation.getPath());

                // Adds the path, genome and rating to the population
                population[i] = new Population(Variables.path, Variables.genome, rating);

                // Draws.
                glcontrol.Refresh();
            }
        }