Example usage for org.apache.commons.math3.genetics Chromosome toString

Introduction

In this page you can find the example usage for org.apache.commons.math3.genetics Chromosome toString.

Prototype

public String toString() 

Source Link

Document

Returns a string representation of the object.

Usage

From source file:com.dlej.Main.java

public static void main(String[] args) {
    long startTime = System.currentTimeMillis();

    // initialize a new genetic algorithm
    GeneticAlgorithm ga = new GeneticAlgorithm(new OnePointCrossover<Character>(), CROSSOVER_RATE,
            new RandomCharacterMutation(), MUTATION_RATE, new TournamentSelection(TOURNAMENT_ARITY));

    // initial population
    Population initial = getInitialPopulation();

    // stopping condition
    StoppingCondition stoppingCondition = new StoppingCondition() {

        int generation = 0;

        @Override/*from  w w w  . jav  a  2s.  co m*/
        public boolean isSatisfied(Population population) {
            Chromosome fittestChromosome = population.getFittestChromosome();

            if (generation == 1 || generation % 10 == 0) {
                System.out.println("Generation " + generation + ": " + fittestChromosome.toString());
            }
            generation++;

            double fitness = fittestChromosome.fitness();
            if (Precision.equals(fitness, 0.0, 1e-6)) {
                return true;
            } else {
                return false;
            }
        }
    };

    System.out.println("Starting evolution ...");

    // run the algorithm
    Population finalPopulation = ga.evolve(initial, stoppingCondition);

    // Get the end time for the simulation.
    long endTime = System.currentTimeMillis();

    // best chromosome from the final population
    Chromosome best = finalPopulation.getFittestChromosome();
    System.out.println("Generation " + ga.getGenerationsEvolved() + ": " + best.toString());
    System.out.println("Total execution time: " + (endTime - startTime) + "ms");
}

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From source file:tmp.GACombPermutation.java

public static void main(String... args) {
    // to test a stochastic algorithm is hard, so this will rather be an usage example

    // initialize a new genetic algorithm
    GeneticAlgorithm ga = new GeneticAlgorithm(new OnePointCrossover<Integer>(), CROSSOVER_RATE,
            new RandomKeyMutation(), MUTATION_RATE, new TournamentSelection(TOURNAMENT_ARITY));

    // initial population
    Population initial = randomPopulation(args);
    System.out.print("Graph e-");
    System.out.print(graph.getEdgeCount());
    System.out.print(" Subgraph e-");
    System.out.println(subgraph.getEdgeCount());

    System.out.println("Initial population:");
    System.out.println(initial.getFittestChromosome());
    long lastime = System.currentTimeMillis();

    // stopping conditions
    StoppingCondition stopCond = new FixedGenerationCount(NUM_GENERATIONS);

    // best initial chromosome
    Chromosome bestInitial = initial.getFittestChromosome();

    // run the algorithm
    //        Population finalPopulation = ga.evolve(initial, stopCond);
    double bestfit = initial.getFittestChromosome().fitness();
    Population current = initial;/*w  w w .j a v  a2 s .  c  o m*/
    int generationsEvolved = 0;
    //        while (!stopCond.isSatisfied(current)) {
    while (bestfit != 0.0) {
        current = ga.nextGeneration(current);
        generationsEvolved++;
        Chromosome bestFinal = current.getFittestChromosome();
        //            System.out.print(bestFinal);
        double atualfit = bestFinal.getFitness();
        if (atualfit > bestfit || System.currentTimeMillis() - lastime > UtilTmp.ALERT_HOUR) {
            lastime = System.currentTimeMillis();
            System.out.print(generationsEvolved);
            System.out.print("-");
            bestfit = atualfit;
            String strbest = bestFinal.toString() + "\n";
            UtilTmp.dumpString(strbest);
            System.out.print(strbest);
            System.out.println();
        }
    }

    // best chromosome from the final population
    Chromosome bestFinal = current.getFittestChromosome();
    System.out.println("Best result:");
    System.out.println(bestFinal);

    // the only thing we can test is whether the final solution is not worse than the initial one
    // however, for some implementations of GA, this need not be true :)
    //        Assert.assertTrue(bestFinal.compareTo(bestInitial) > 0);
    //System.out.println(bestInitial);
    //System.out.println(bestFinal);
}

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