Example usage for org.apache.commons.math3.genetics GeneticAlgorithm getGenerationsEvolved

Introduction

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

Prototype

public int getGenerationsEvolved() 

Source Link

Document

Returns the number of generations evolved to reach StoppingCondition in the last run.

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  ww  w  .ja v  a2s  .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: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 ww w .j  a 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: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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