Examples of Population

aufnahme.fmg.edu.kit.aifb.AufnahmeTyp.Population
com.heatonresearch.aifh.evolutionary.population.Population
Defines a population of genomes.

A Population is the repository for all the Individuals being bred or evaluated in the evolutionary run at a given time. A Population is basically an array of Subpopulations, each of which are arrays of Individuals coupled with a single Species per Subpoulation.

The first Population is created using the initializePopulation method of the Initializer object, which typically calls the Population's populate() method in turn. On generational systems, subsequent populations are created on a generation-by-generation basis by the Breeder object, replacing the previous Population.

In a multithreaded area of a run, Populations should be considered immutable. That is, once they are created, they should not be modified, nor anything they contain. This protocol helps ensure read-safety under multithreading race conditions.

Parameters

base.`subpops` int >= 1	(the number of subpopulations)
base.`subpop`.n classname, inherits or = ec.Subpopulation	(the class for subpopulation #n)
base.`default-subpop` int >= 0	(the default subpopulation index. The parameter base of this subpopulation will be used as the default base for all subpopulations which do not define one themselves./tr>

Parameter bases

base.subpop.n Subpopulation #n.

@author Sean Luke @version 1.0

jcgp.backend.population.Population
This class primarily holds a collection of chromosomes. In addition, it provides a few utility methods for manipulating and copying chromosomes, useful for evolutionary strategies.

{@code copyChromosome()} is used to create copies of chromosomes, though it is also possible to create a new instance of population directly from a seed chromosome using the right constructor.

For convenience, a random chromosome can be retrieved using {@code getRandomChromosome()}, which is guaranteed to use the experiment's specified seed. If an entirely random population is needed, {@code reinitialise()} should be used to randomiseall chromosomes without creating a new instance of {@code Population}. @author Eduardo Pedroni
org.apache.commons.math3.genetics.Population
A collection of chromosomes that facilitates generational evolution. @since 2.0
org.encog.ml.ea.population.Population
Defines a population of genomes.
org.encog.ml.genetic.population.Population
Defines a population of genomes.
ru.mail.teodorgig.mididermi.base.Population
Differential evolution population class. Implementation of genetic algorithm based optimization approach. All melodies are presented as chromosomes (vectors). During evolution period recombination of chromosomes is applied and human evaluation of the melody quality. @author Todor Balabanov @email teodorgig@mail.ru @date 22 May 2008
wolf.city.map.Population

Examples of aufnahme.fmg.edu.kit.aifb.AufnahmeTyp.Population

            this.xmlAufn.getAufnahme().addNewAusgaben();
        }
        Kommandozeilenausgaben ausg 
            = this.xmlAufn.getAufnahme().getAusgaben();
        byte[] xmlFeld;
        Population aktPop;
        Calendar zeit;
        String sens;
        
        // Parameterliste:
        this.xmlAufn.getAufnahme().setParameterliste(
                this.pars.parStrPlain());


        // Zeitstempel:
        zeit = Calendar.getInstance();
        zeit.setTime(this.datum);
        this.xmlAufn.getAufnahme().setTimestamp(zeit);
        
        // Kommandozeilenausgaben (danach flushen):
        for (AbstractMsg m : this.pars.getMsgs(
                null, 
                Long.MIN_VALUE, 
                Long.MAX_VALUE)) {
            ausg.addNewEinzelzeile();
            ausg.getEinzelzeileArray(ausg.getEinzelzeileArray().length - 1)
                .setLogStufe(m.getType());
            ausg.getEinzelzeileArray(ausg.getEinzelzeileArray().length - 1)
                .setText(m.getMessage());
            ausg.getEinzelzeileArray(ausg.getEinzelzeileArray().length - 1)
                .setWeiteres("" + m.getZusatz());
            
            zeit = Calendar.getInstance();
            zeit.setTimeInMillis(m.getDatum());
            
            ausg.getEinzelzeileArray(ausg.getEinzelzeileArray().length - 1)
                .setZeitstempel(zeit);
        }
        this.pars.flushMsgs();


        // Feld:
        if (this.xmlAufn.getAufnahme().getUmgebung() == null) {
            this.xmlAufn.getAufnahme().addNewUmgebung();
            int x = this.umgebung.getFeld().length;
            int y = this.umgebung.getFeld()[0].length;
            this.xmlAufn.getAufnahme().getUmgebung().addNewGroesse();
            this.xmlAufn.getAufnahme().getUmgebung().getGroesse().setX(x);
            this.xmlAufn.getAufnahme().getUmgebung().getGroesse().setY(y);
            byte[][] inpFeld = this.umgebung.getInputFeld();
            
            xmlFeld = new byte[x * y];
            for (int i = 0; i < x; i++) {
                for (int j = 0; j < y; j++) {
                    xmlFeld[i * y + j] = inpFeld[i][j];
                }
            }
            
            try {
                this.xmlAufn.getAufnahme().getUmgebung().setFeld(
                        StaticMethods.compressByteStream(xmlFeld, this.pars));
            } catch (Exception e) {
                e.printStackTrace();
            }
        }
        
        // Populationen (danach flushen):
        for (PopSnapshot p : this.populationen) {
            int popID;
            this.xmlAufn.getAufnahme().addNewPopulation();
            aktPop = this.xmlAufn.getAufnahme().getPopulationArray(
                    this.xmlAufn.getAufnahme().getPopulationArray().length - 1);
            aktPop.setCycle(p.getId());
            popID = this.xmlAufn.getAufnahme().getPopulationArray().length - 1;
            aktPop.setID(popID);
            
            // Verschiebung der Gegenst�nde:
            if (aktPop.getGegVersch() == null) {
                aktPop.addNewGegVersch();
            }
            for (int i = 0; i < p.getVerschiebungen().length; i++) {
                Vector2D aktVersch = p.getVerschiebungen()[i];
                aktPop.getGegVersch().addNewVerschiebung();
                aktPop.getGegVersch().getVerschiebungArray()[i]
                     .setX(aktVersch.x);
                aktPop.getGegVersch().getVerschiebungArray()[i]
                     .setY(aktVersch.y);
                aktPop.getGegVersch().getVerschiebungArray()[i].setID(i);
            }
            
            // Dynamische Wände:
            if (aktPop.getDynWaende() == null) {
                DynWaendeTyp waende;
                DynWandTyp wand;
                waende = aktPop.addNewDynWaende();
                for (int i = 0; i < p.getTatdynDrehMittPkt().length; i++) {
                    if (p.getTatdynDrehMittPkt()[i] != null) {
                        wand = waende.addNewWand();
                        wand.setID(i);
                        wand.addNewDynDrehMittelpunkt();
                        wand.getDynDrehMittelpunkt().setX(
                                p.getTatdynDrehMittPkt()[i].x);
                        wand.getDynDrehMittelpunkt().setY(
                                p.getTatdynDrehMittPkt()[i].y);
                        wand.addNewDynSkalierung();
                        wand.getDynSkalierung().setX(
                                p.getTatdynSkalFaktor()[i].x);
                        wand.getDynSkalierung().setY(
                                p.getTatdynSkalFaktor()[i].y);
                        wand.addNewDynVersch();
                        wand.getDynVersch().setX(p.getTatdynVersch()[i].x);
                        wand.getDynVersch().setY(p.getTatdynVersch()[i].y);
                        wand.setDynDrehWinkel(p.getTatdynDrehWinkel()[i]);
                    }
                }
            }
            
            // Roboter:
            for (RobSnapshot r : p.getRobSchnapp()) {
                Roboter neuRob;
                aktPop.addNewRoboter();
                neuRob = aktPop
                        .getRoboterArray(aktPop.getRoboterArray().length - 1);
                // ID:
                neuRob.setID(r.getId());
                neuRob = aktPop
                        .getRoboterArray(aktPop.getRoboterArray().length - 1);
                // Position:
                neuRob.addNewPos();
                neuRob.getPos().setWinkel(r.getWinkel());
                neuRob.getPos().setX(r.getPosition().x);
                neuRob.getPos().setY(r.getPosition().y);

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Examples of com.heatonresearch.aifh.evolutionary.population.Population

        System.out.println("Mutate Perturb");


        GenerateRandom rnd = new MersenneTwisterGenerateRandom();


        // Create a new population.
        Population pop = new BasicPopulation();
        pop.setGenomeFactory(new DoubleArrayGenomeFactory(5));


        // Create a trainer with a very simple score function.  We do not care
        // about the calculation of the score, as they will never be calculated.
        EvolutionaryAlgorithm train = new BasicEA(pop, new ScoreFunction() {
            @Override
            public double calculateScore(MLMethod method) {
                return 0;
            }


            @Override
            public boolean shouldMinimize() {
                return false;
            }
        });




        MutatePerturb opp = new MutatePerturb(0.1);
        train.addOperation(1.0, opp);




        // Create a peterb operator.  Use it 1.0 (100%) of the time.
        DoubleArrayGenome[] parents = new DoubleArrayGenome[1];
        parents[0] = (DoubleArrayGenome) pop.getGenomeFactory().factor();
        parents[0].setPopulation(pop);


        for (int i = 1; i <= 5; i++) {
            parents[0].getData()[i - 1] = i;
        }

View Full Code Here

Examples of com.heatonresearch.aifh.evolutionary.population.Population

     * Create an initial random population of random paths through the cities.
     *
     * @return The random population.
     */
    private Population initPopulation() {
        Population result = new BasicPopulation(POPULATION_SIZE, null);


        BasicSpecies defaultSpecies = new BasicSpecies();
        defaultSpecies.setPopulation(result);
        for (int i = 0; i < POPULATION_SIZE; i++) {
            final IntegerArrayGenome genome = randomGenome();
            defaultSpecies.add(genome);
        }
        result.setGenomeFactory(new IntegerArrayGenomeFactory(cities.length));
        result.getSpecies().add(defaultSpecies);


        return result;
    }

View Full Code Here

Examples of com.heatonresearch.aifh.evolutionary.population.Population

    public void solve() {
        StringBuilder builder = new StringBuilder();


        initCities();


        Population pop = initPopulation();


        ScoreFunction score = new TSPScore(cities);


        genetic = new BasicEA(pop, score);

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Examples of com.heatonresearch.aifh.evolutionary.population.Population

        // Create a RBF network to get the length.
        final RBFNetwork network = new RBFNetwork(codec.getInputCount(), codec.getRbfCount(), codec.getOutputCount());
        int size = network.getLongTermMemory().length;


        // Create a new population, use a single species.
        Population result = new BasicPopulation(POPULATION_SIZE, new DoubleArrayGenomeFactory(size));
        BasicSpecies defaultSpecies = new BasicSpecies();
        defaultSpecies.setPopulation(result);
        result.getSpecies().add(defaultSpecies);


        // Create a new population of random networks.
        for (int i = 0; i < POPULATION_SIZE; i++) {
            final DoubleArrayGenome genome = new DoubleArrayGenome(size);
            network.reset(rnd);
            System.arraycopy(network.getLongTermMemory(), 0, genome.getData(), 0, size);
            defaultSpecies.add(genome);
        }


        // Set the genome factory to use the double array genome.
        result.setGenomeFactory(new DoubleArrayGenomeFactory(size));


        return result;


    }

View Full Code Here

Examples of com.heatonresearch.aifh.evolutionary.population.Population

            final RBFNetworkGenomeCODEC codec = new RBFNetworkGenomeCODEC(4, 4, 3);


            final List<BasicData> trainingData = ds.extractSupervised(0,
                    codec.getInputCount(), codec.getRbfCount(), codec.getOutputCount());


            Population pop = initPopulation(rnd, codec);


            ScoreFunction score = new ScoreRegressionData(trainingData);


            BasicEA genetic = new BasicEA(pop, score);
            genetic.setSpeciation(new ArraySpeciation<DoubleArrayGenome>());

View Full Code Here

Examples of com.heatonresearch.aifh.evolutionary.population.Population

        // Create a RBF network to get the length.
        final RBFNetwork network = new RBFNetwork(codec.getInputCount(), codec.getRbfCount(), codec.getOutputCount());
        int size = network.getLongTermMemory().length;


        // Create a new population, use a single species.
        Population result = new BasicPopulation(POPULATION_SIZE, new DoubleArrayGenomeFactory(size));
        BasicSpecies defaultSpecies = new BasicSpecies();
        defaultSpecies.setPopulation(result);
        result.getSpecies().add(defaultSpecies);


        // Create a new population of random networks.
        for (int i = 0; i < POPULATION_SIZE; i++) {
            final DoubleArrayGenome genome = new DoubleArrayGenome(size);
            network.reset(rnd);
            System.arraycopy(network.getLongTermMemory(), 0, genome.getData(), 0, size);
            defaultSpecies.add(genome);
        }


        // Set the genome factory to use the double array genome.
        result.setGenomeFactory(new DoubleArrayGenomeFactory(size));


        return result;


    }

View Full Code Here

Examples of com.heatonresearch.aifh.evolutionary.population.Population

            final RBFNetworkGenomeCODEC codec = new RBFNetworkGenomeCODEC(4, RBF_COUNT, 3);


            final List<BasicData> trainingData = ds.extractSupervised(0,
                    codec.getInputCount(), 4, codec.getOutputCount());


            Population pop = initPopulation(rnd, codec);


            ScoreFunction score = new ScoreRegressionData(trainingData);


            BasicEA genetic = new BasicEA(pop, score);
            genetic.setCODEC(codec);

View Full Code Here

Examples of com.heatonresearch.aifh.evolutionary.population.Population

     * @param rnd  A random number generator.
     * @param eval The expression evaluator.
     * @return The new population.
     */
    private Population initPopulation(GenerateRandom rnd, EvaluateExpression eval) {
        Population result = new BasicPopulation(POPULATION_SIZE, null);


        BasicSpecies defaultSpecies = new BasicSpecies();
        defaultSpecies.setPopulation(result);
        for (int i = 0; i < POPULATION_SIZE; i++) {
            final TreeGenome genome = randomGenome(rnd, eval);
            defaultSpecies.add(genome);
        }
        result.setGenomeFactory(new TreeGenomeFactory(eval));
        result.getSpecies().add(defaultSpecies);


        return result;
    }

View Full Code Here

Examples of com.heatonresearch.aifh.evolutionary.population.Population

        List<BasicData> training = ds.extractSupervised(0, 1, 1, 1);




        GenerateRandom rnd = new MersenneTwisterGenerateRandom();
        EvaluateExpression eval = new EvaluateExpression(rnd);
        Population pop = initPopulation(rnd, eval);
        ScoreFunction score = new ScoreSmallExpression(training,30);


        EvolutionaryAlgorithm genetic = new BasicEA(pop, score);
        genetic.addOperation(0.3, new MutateTree(3));
        genetic.addOperation(0.7, new CrossoverTree());

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