Package com.heatonresearch.aifh.evolutionary.population

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

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        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;
        }
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     * 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;
    }
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    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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        // 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;

    }
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            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>());
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        // 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;

    }
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            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);
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     * @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;
    }
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        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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     * Create the initial random population.
     *
     * @return The population.
     */
    private Population initPopulation() {
        Population result = new BasicPopulation(PlantUniverse.POPULATION_SIZE, null);

        BasicSpecies defaultSpecies = new BasicSpecies();
        defaultSpecies.setPopulation(result);
        for (int i = 0; i < PlantUniverse.POPULATION_SIZE; i++) {
            final DoubleArrayGenome genome = randomGenome();
            defaultSpecies.add(genome);
        }
        result.setGenomeFactory(new DoubleArrayGenomeFactory(PlantUniverse.GENOME_SIZE));
        result.getSpecies().add(defaultSpecies);

        return result;
    }
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Related Classes of com.heatonresearch.aifh.evolutionary.population.Population

  • com.heatonresearch.aifh.examples.capstone.alife.milestone3.Milestone3Main
  • com.heatonresearch.aifh.examples.ga.iris.ModelIris
  • com.heatonresearch.aifh.examples.ga.iris.ModelSpeciationIris
  • com.heatonresearch.aifh.examples.ga.tsp.GeneticTSPExample
  • com.heatonresearch.aifh.examples.gp.FindEquation
  • com.heatonresearch.aifh.examples.operations.CrossoverExample
  • com.heatonresearch.aifh.examples.operations.MutateExample
  • com.heatonresearch.aifh.examples.selection.TournamentCompareExample
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