Example usage for org.apache.commons.math3.genetics BinaryMutation BinaryMutation

Introduction

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

Prototype

BinaryMutation

Source Link

Usage

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));
    }//from www. j  ava 2s .c  om
    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: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/*  w  ww .  j a  va2 s  . c  o 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: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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