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

Introduction

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

Prototype

int getPopulationSize();

Source Link

Document

Access the current population size.

Usage

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

/**
 * Evolve the given population into the next generation.
 * <p>/*from   w  w  w  .  j  a v a2  s .  co m*/
 * <ol>
 *  <li>Get nextGeneration population to fill from <code>current</code>
 *      generation, using its nextGeneration method</li>
 *  <li>Loop until new generation is filled:</li>
 *  <ul><li>Apply configured SelectionPolicy to select a pair of parents
 *          from <code>current</code></li>
 *      <li>With probability = {@link #getCrossoverRate()}, apply
 *          configured {@link CrossoverPolicy} to parents</li>
 *      <li>With probability = {@link #getMutationRate()}, apply
 *          configured {@link MutationPolicy} to each of the offspring</li>
 *      <li>Add offspring individually to nextGeneration,
 *          space permitting</li>
 *  </ul>
 *  <li>Return nextGeneration</li>
 * </ol>
 *
 * @param current the current population.
 * @return the population for the next generation.
 */
public Population nextGeneration(final Population current) {
    Population nextGeneration = current.nextGeneration();

    RandomGenerator randGen = getRandomGenerator();

    while (nextGeneration.getPopulationSize() < nextGeneration.getPopulationLimit()) {
        // select parent chromosomes
        ChromosomePair pair = getSelectionPolicy().select(current);
        //System.out.println("*****" + pair);

        // crossover?
        if (randGen.nextDouble() < getCrossoverRate()) {
            // apply crossover policy to create two offspring
            pair = getCrossoverPolicy().crossover(pair.getFirst(), pair.getSecond());
        }

        // mutation?
        if (randGen.nextDouble() < getMutationRate()) {
            // apply mutation policy to the chromosomes
            pair = new ChromosomePair(getMutationPolicy().mutate(pair.getFirst()),
                    getMutationPolicy().mutate(pair.getSecond()));
        }

        // add the first chromosome to the population
        nextGeneration.addChromosome(pair.getFirst());
        // is there still a place for the second chromosome?
        if (nextGeneration.getPopulationSize() < nextGeneration.getPopulationLimit()) {
            // add the second chromosome to the population
            nextGeneration.addChromosome(pair.getSecond());
        }
    }

    System.out.println("\nGeneration (iteration): " + (generationsEvolved + 1));
    WSN.printPopulation(nextGeneration);

    return nextGeneration;
}

---

From source file:it.units.malelab.sse.MyGeneticAlgorithm.java

@Override
public Population evolve(Population initial, StoppingCondition condition) {
    Population current = initial;
    generationsEvolved = 0;/*from w w  w .jav  a 2s.  c  om*/
    while (!condition.isSatisfied(current)) {
        current = nextGeneration(current);
        generationsEvolved++;
        //obtain stats
        List<EnumMap<Evaluator.ResultType, Double>> statsList = new ArrayList<>(current.getPopulationSize());
        Iterator<Chromosome> iterator = current.iterator();
        while (iterator.hasNext()) {
            OperationsChromosome chromosome = (OperationsChromosome) iterator.next();
            EnumMap<Evaluator.ResultType, Double> stats = chromosome.getStats();
            if (stats.containsKey(Evaluator.ResultType.OVERLAPNESS)) {
                statsList.add(stats);
            }
        }
        Collections.sort(statsList, new Comparator<EnumMap<Evaluator.ResultType, Double>>() {
            @Override
            public int compare(EnumMap<Evaluator.ResultType, Double> stats1,
                    EnumMap<Evaluator.ResultType, Double> stats2) {
                return Double.compare(stats1.get(Evaluator.ResultType.OVERLAPNESS),
                        stats2.get(Evaluator.ResultType.OVERLAPNESS));
            }
        });
        EnumMap<Evaluator.ResultType, Double> bestStats = statsList.get(0);
        EnumMap<Evaluator.ResultType, Double> top10Stats = mean(statsList.subList(0, 10));
        EnumMap<Evaluator.ResultType, Double> allStats = mean(statsList);
        System.out.printf("ovp=%5.3f/%5.3f/%5.3f   ", bestStats.get(Evaluator.ResultType.OVERLAPNESS),
                top10Stats.get(Evaluator.ResultType.OVERLAPNESS),
                allStats.get(Evaluator.ResultType.OVERLAPNESS));
        System.out.printf("ops=%4.0f/%4.0f/%4.0f   ", bestStats.get(Evaluator.ResultType.AVG_OPS),
                top10Stats.get(Evaluator.ResultType.AVG_OPS), allStats.get(Evaluator.ResultType.AVG_OPS));
        System.out.printf("mfp=%4.0f/%4.0f/%4.0f   ", bestStats.get(Evaluator.ResultType.AVG_FOOTPRINT),
                top10Stats.get(Evaluator.ResultType.AVG_FOOTPRINT),
                allStats.get(Evaluator.ResultType.AVG_FOOTPRINT));
        System.out.printf("err=%5.3f/%5.3f/%5.3f   ", bestStats.get(Evaluator.ResultType.ERROR_RATIO),
                top10Stats.get(Evaluator.ResultType.ERROR_RATIO),
                allStats.get(Evaluator.ResultType.ERROR_RATIO));
        System.out.printf("size=%3.0f/%3.0f/%3.0f   ", bestStats.get(Evaluator.ResultType.SIZE),
                top10Stats.get(Evaluator.ResultType.SIZE), allStats.get(Evaluator.ResultType.SIZE));
        System.out.printf("evals=%8d\n", evaluator.getEvaluatedCount());
        //System.out.println(evaluator.getErrorCodes());
    }
    return current;
}

---