Example usage for org.apache.commons.math3.genetics Population getFittestChromosome

Introduction

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

Prototype

Chromosome getFittestChromosome();

Source Link

Document

Access the fittest chromosome in this population.

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  . j av  a 2 s .c o  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:it.units.malelab.sse.Main.java

public static void main(String[] args) throws IOException {
    Random random = new Random(1);
    VirtualMachine vm = new VirtualMachine(4, 4, 400);
    List<Map<Boolean, List<String>>> datasets = new ArrayList<>();
    datasets.add(Util.loadStrings("/home/eric/Documenti/esperimenti/datasets/Bills-Date.txt", random));
    datasets.add(Util.loadStrings("/home/eric/Documenti/esperimenti/datasets/Log-IP.txt", random));
    datasets.add(Util.loadStrings("/home/eric/Documenti/esperimenti/datasets/Twitter-URL.txt", random));

    Evaluator evaluator = new Evaluator(vm, datasets, 1, 10);

    MyGeneticAlgorithm ga = new MyGeneticAlgorithm(new OnePointCrossover<Integer>(), 0.2, new BinaryMutation(),
            0.6, new TournamentSelection(10), evaluator);
    MyGeneticAlgorithm.setRandomGenerator(new JDKRandomGenerator(1));

    List<Chromosome> chromosomes = new ArrayList<>();
    for (int i = 0; i < 2000; i++) {
        chromosomes.add(new OperationsChromosome(evaluator));
    }// w w w. ja  va 2s.  com
    Population population = new ElitisticListPopulation(chromosomes, chromosomes.size(), 0.99);
    Population finalPopulation = ga.evolve(population, new FixedGenerationCount(10000));
    List<Operation> operations = ((OperationsChromosome) finalPopulation.getFittestChromosome())
            .getOperations();
    for (int i = 0; i < operations.size(); i++) {
        System.out.printf("%4d: %s\n", i, operations.get(i));
    }

}

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From source file:eu.tsp.sal.WSN.java

/**
 * Algorithm run on a number of sensors = LENGTH
 * //from ww  w . j  a  v  a 2  s  .c o  m
 * Population has POPULATION_SIZE individuals (SensorIndividual class)
 * each individual has LENGTH genes (~ sensor)
 * 
 *  
 * 
 */

public static void main(String[] args) {
    /**
     *  initialize a new genetic algorithm with
     *      Crossover policy
     *      CROSSOVER_RATE
     *      Mutation policy
     *      MUTATION_RATE
     *      Selection Policy 
     *      
     */

    SensorGeneticAlgorithm ga = new SensorGeneticAlgorithm(new NPointCrossover(2), CROSSOVER_RATE, // all selected chromosomes will be recombined (=crosssover)
            new SensorMutation(), MUTATION_RATE, new SensorTournamentSelection(TOURNAMENT_ARITY));

    //assertEquals(0, ga.getGenerationsEvolved());
    //System.out.println(ga.getGenerationsEvolved());

    // initial population of POPULATION_SIZE SensorIndividual
    //Population initial = randomPopulation(LENGTH, POPULATION_SIZE);
    Population initial = randomPopulationWithFixedOA(OA_NUMBER, LENGTH, POPULATION_SIZE);

    printPopulation(initial);

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

    // best initial chromosome
    Chromosome bestInitial = initial.getFittestChromosome();
    System.out.println("Best Individual in initial population (highest fitness) = " + bestInitial);
    System.out.println("Solution  of the best individual = " + ((SensorIndividual) bestInitial).solution());

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

    // best SensorIndividual from the final population
    Chromosome bestFinal = finalPopulation.getFittestChromosome();
    System.out.println("\nBest Individual in final population (highest fitness) = " + bestFinal);
    //System.out.println("Solution of the best individual = " + ((SensorIndividual) bestFinal).solution());
    printSolution((SensorIndividual) bestFinal);

    // Assertion

    // assertTrue(bestFinal.compareTo(bestInitial) > 0);
    // assertEquals(NUM_GENERATIONS, ga.getGenerationsEvolved());
    System.out.println((bestFinal.compareTo(bestInitial) > 0) ? "Final generation is better than the initial"
            : "Final generation is worse than the initial!!!!");
    //System.out.println(ga.getGenerationsEvolved());
}

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From source file:ga.GeneticAlgorithmTestBinary.java

@Test
public void test() {
    // to test a stochastic algorithm is hard, so this will rather be an usage example

    // initialize a new genetic algorithm
    GeneticAlgorithm ga = new StatisticGeneticAlgorithm(new OnePointCrossover<Integer>(), CROSSOVER_RATE, // all selected chromosomes will be recombined (=crosssover)
            new BinaryMutation(), MUTATION_RATE, new TournamentSelection(TOURNAMENT_ARITY));

    Assert.assertEquals(0, ga.getGenerationsEvolved());

    // initial population
    Population initial = randomPopulation();
    // stopping conditions
    StoppingCondition stopCond = new FixedGenerationCount(NUM_GENERATIONS);

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

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

    // best chromosome from the final population
    Chromosome bestFinal = finalPopulation.getFittestChromosome();

    // the only thing we can test is whether the final solution is not worse than the initial
    // one//from   w ww  . ja  v  a  2  s . co m
    // however, for some implementations of GA, this need not be true :)

    Assert.assertTrue(bestFinal.compareTo(bestInitial) > 0);
    Assert.assertEquals(NUM_GENERATIONS, ga.getGenerationsEvolved());
    System.out.println(bestFinal);

}

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From source file:org.apache.kylin.cube.cuboid.algorithm.generic.GeneticAlgorithm.java

@Override
public List<Long> start(double maxSpaceLimit) {
    logger.debug("Genetic Algorithm started.");

    //Initial mandatory cuboids
    double remainingSpace = maxSpaceLimit;
    for (Long mandatoryOne : cuboidStats.getAllCuboidsForMandatory()) {
        if (cuboidStats.getCuboidSize(mandatoryOne) != null) {
            remainingSpace -= cuboidStats.getCuboidSize(mandatoryOne);
        }//  w  w w  .ja v a 2s .  c  o m
    }

    BitsChromosomeHelper helper = new BitsChromosomeHelper(remainingSpace, cuboidStats);

    //Generate a population randomly
    Population initial = initRandomPopulation(helper);

    //Set stopping condition
    List<StoppingCondition> conditions = Lists.newArrayList();
    conditions.add(new FixedGenerationCount(550));
    CombinedStoppingCondition stopCondition = new CombinedStoppingCondition(conditions);

    //Start the evolution
    Population current = geneticAlgorithm.evolve(initial, stopCondition);
    BitsChromosome chromosome = (BitsChromosome) current.getFittestChromosome();
    logger.debug("Genetic Algorithm finished.");
    List<Long> finalList = Lists.newArrayList();
    finalList.addAll(helper.getMandatoryCuboids());
    finalList.addAll(chromosome.getCuboids());

    double totalSpace = 0;
    if (logger.isTraceEnabled()) {
        for (Long cuboid : finalList) {
            Double unitSpace = cuboidStats.getCuboidSize(cuboid);
            if (unitSpace != null) {
                logger.trace(String.format(Locale.ROOT, "cuboidId %d and Space: %f", cuboid, unitSpace));
                totalSpace += unitSpace;
            } else {
                logger.trace(String.format(Locale.ROOT, "mandatory cuboidId %d", cuboid));
            }
        }
        logger.trace("Total Space:" + totalSpace);
        logger.trace("Space Expansion Rate:" + totalSpace / cuboidStats.getBaseCuboidSize());
    }
    return finalList;
}

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From source file:p.lodz.playground.ApacheGeneticsTest.java

@Test
public void test() {
    // 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, // all
            // selected
            // chromosomes
            // will be
            // recombined
            // (=crosssover)
            new BinaryMutation(), MUTATION_RATE, new TournamentSelection(TOURNAMENT_ARITY));

    Assert.assertEquals(0, ga.getGenerationsEvolved());

    // initial population
    Population initial = randomPopulation();
    // stopping conditions
    StoppingCondition stopCond = new FixedGenerationCount(NUM_GENERATIONS);

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

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

    // best chromosome from the final population
    Chromosome bestFinal = finalPopulation.getFittestChromosome();

    // 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);
    Assert.assertEquals(NUM_GENERATIONS, ga.getGenerationsEvolved());

    System.out.println(bestFinal);
}

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From source file:tmp.GACombGraphMoore.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.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;//from  w w  w .j  a va2s . 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 > HOUR) {
            bestfit = atualfit;
            //                String strbest = generationsEvolved + "-f=" + atualfit + "-" + ((MinPermutations) bestFinal).decode(sequence).toString().replaceAll(" ", "") + "\n";
            String strbest = generationsEvolved + "-f=" + atualfit;
            //                UtilTmp.dumpString(strbest);
            System.out.println(strbest);
            //                strbest = bestFinal.toString();
            //                UtilTmp.dumpString(strbest);
            System.out.println(strbest);
            //                System.out.println();
            lastime = System.currentTimeMillis();
        }
    }

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

    // 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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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 .  ja v  a2s . co  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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From source file:tmp.GeneticAlgorithmTestPermutations.java

@Test
public void test() {
    // 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();
    System.out.print("Initial population");
    System.out.println(initial.getFittestChromosome());

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

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

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

    // best chromosome from the final population
    Chromosome bestFinal = finalPopulation.getFittestChromosome();
    System.out.print("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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