Exemplo n.º 1
0
        int zerosSum; // how many inactive genes are in chromozome?

        #endregion Fields

        #region Constructors

        /// <summary>
        /// Creates new random population and sets parameters of evolution
        /// </summary>
        /// <param name="populationSize">Population size</param>
        /// <param name="probCrossOver">Probability of crossover</param>
        /// <param name="probMutability">Probability of mutation</param>
        /// <param name="elitism">How many best ones will survive?</param>
        /// <param name="shareSigma">Distance, two chromozome with distance below that parameter are similiar</param>
        /// <param name="shareAlfa">Sharing constant</param>
        /// <param name="scaleSigma">Scaling constant, best fitnuss would be scaleSigma times greater than average population fitness</param>
        /// <param name="newBorn">True if extincted ones should be replace by random ones</param>
        /// <param name="zeros">Which genes arent active in this evolution? (ex. building fort is banned in a scenario)</param>
        public Genetic(int populationSize, double probCrossOver, double probMutability, int elitism, double shareSigma, double shareAlfa, double scaleSigma, bool newBorn, bool [] zeros)
        {
            rnd = new Random();
            this.shareSigma = shareSigma;
            this.shareAlfa = shareAlfa;
            this.scaleSigma = scaleSigma;
            this.elitism = elitism;
            this.probCrossOver = probCrossOver;
            this.probMutability = probMutability;
            this.populationSize = populationSize;
            this.extinction = 0.0;
            this.newBorn = newBorn;
            this.zeros = zeros;
            generationNumber = 0;

            zerosSum = 0;
            for (int loop1 = 0; loop1 < zeros.Length; loop1++)
                if (zeros[loop1])
                    zerosSum++;

            population = new Chromozone[populationSize];

            // generate random start population
            for (int loop1 = 0; loop1 < populationSize; loop1++)
                population[loop1] = new Chromozone(rnd, zeros);

            theBestOne = population[0];
            fitnessBestOne = 0.0;

            activeChromozomeID = 0;
            lastFitness = 0.0;

            // start time of evolving
            stopwatch = new Stopwatch();
            stopwatch.Start();
        }
Exemplo n.º 2
0
        private void NewPopulation()
        {
            // create empty list of new population
            List<Chromozone> newPopulation = new List<Chromozone>();

            // sort chromozomes according their fitness
            Array.Sort(population);
            Log();
            if (generationNumber % 3 == 0) // in each third generation, print all chromozomes in text file
                PrintAllChromozones();

            // replace best one in all populations
            if (population[0].GetFitness() > fitnessBestOne)
            {
                fitnessBestOne = population[0].GetFitness();
                theBestOne = population[0];
            }

            // At least the worst one wont produce itself
            // Adds something to exctinction
            for (int loop1 = population.Length - 1; loop1 >= population.Length / 3; loop1--)
            {
                if (population[loop1].GetFitness() > extinction &&
                    population[loop1].GetFitness() != population[population.Length / 3 - 1].GetFitness())
                {
                    extinction = population[loop1].GetFitness();
                    break;
                }
            }

            // apply elitism
            for (int loop1 = 0; loop1 < elitism; loop1++)
            {
                newPopulation.Add(new Chromozone(population[loop1].GetGenes(), zeros));
            }

            // kill all chromozome with fitness less than extinction value
            KillTheWorsts(extinction);

            // replace extincted ones by new random ones
            if (newBorn || population.Length < 2)
                AddFreshOnes(newPopulation);

            // linear scaling of fitness
            ScaleFactor();
            // change fitnesses according share value
            ShareFactor();

            // prepare roulete wheel for selectioin
            double sumFitness = 0.0;
            double sumProb = 0.0;
            foreach (Chromozone ch in population)
            {
                sumFitness += ch.GetFitness();
            }
            foreach (Chromozone ch in population)
            {
                double prob = sumProb + ch.GetFitness() / sumFitness;
                ch.SetProbability(prob);
                sumProb += ch.GetFitness() / sumFitness;
            }
            // end of preparing roulete wheel

            int[][][] sons;
            while (newPopulation.Count < populationSize)
            {
                // clone selected mum and dad
                int[][] dad = Chromozone.CloneArray2D(Selection());
                int[][] mum = Chromozone.CloneArray2D(Selection());

                sons = CrossOver(dad, mum);

                for (int loop1 = 0; loop1 < sons.Length; loop1++)
                {
                    sons[loop1] = Mutation(sons[loop1]);
                    newPopulation.Add(new Chromozone(sons[loop1], zeros));
                }
            }

            population = newPopulation.ToArray();

            // when there are exactly same chromozome in new population, mutate the copyiest until thera arent two same chromozomes
            DifferPopulation();
        }
Exemplo n.º 3
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 /// <summary>
 /// For the last step of evolution new generation.
 /// If there are 2 exactly same chromozomes, mutate one of them.
 /// </summary>
 private void DifferPopulation()
 {
     double tempMutability = probMutability;
     probMutability = 0.1;
     for (int loop1 = 0; loop1 < population.Length; loop1++)
     {
         for (int loop2 = 0; loop2 < population.Length; loop2++)
         {
             if (loop1 != loop2 && population[loop1].DistanceTo(population[loop2]) == 0)
             {
                 population[loop1] = new Chromozone(Mutation(population[loop1].GetGenes()), zeros);
                 loop2 = 0;
             }
         }
     }
     probMutability = tempMutability;
 }
Exemplo n.º 4
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        private void KillTheWorsts(double trashhold)
        {
            int alive = 0;
            // how many will survive?
            for (alive = 0; alive < population.Length; alive++)
            {
                if (population[alive].GetFitness() < trashhold)
                {
                    break;
                }
            }

            // copy survived chromozome to new array and replace with it the old one
            if (alive != population.Length)
            {
                Chromozone[] oldPopulation = new Chromozone[alive];
                for (int loop1 = 0; loop1 < oldPopulation.Length; loop1++)
                {
                    oldPopulation[loop1] = population[loop1];
                }
                population = oldPopulation;
            }
        }
Exemplo n.º 5
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        /// <summary>
        /// Returns weighted manhatton distance between two chromozomes
        /// </summary>
        /// <param name="b">Second chromozome</param>
        /// <returns>Distance</returns>
        internal double DistanceTo(Chromozone b)
        {
            double chromDistance = 0.0;
            for (int loop1 = 0; loop1 < genes.Length; loop1++)
            {
                for (int loop2 = 0; loop2 < genes[loop1].Length; loop2++)
                {
                    int genDistance = Math.Abs(genes[loop1][loop2] - b.genes[loop1][loop2]);

                    if (loop2 == 0)
                    {
                        chromDistance += genDistance;
                    }
                    else
                    {
                        chromDistance += 5 * genDistance;
                    }
                }
            }
            return chromDistance;
        }