Package com.heatonresearch.aifh.evolutionary.population

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

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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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        // 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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        // 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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     * @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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     * 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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        // Create a random number generator
        GenerateRandom rnd = new MersenneTwisterGenerateRandom();

        // Create a new population.
        Population pop = new BasicPopulation();
        pop.setGenomeFactory(new IntegerArrayGenomeFactory(10));

        // 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;
            }
        });

        // Create a splice operator, length = 5.  Use it 1.0 (100%) of the time.
        Splice opp = new Splice(5);
        train.addOperation(1.0, opp);

        // Create two parents, the genes are set to 1,2,3,4,5,7,8,9,10
        // and 10,9,8,7,6,5,4,3,2,1.
        IntegerArrayGenome[] parents = new IntegerArrayGenome[2];
        parents[0] = (IntegerArrayGenome) pop.getGenomeFactory().factor();
        parents[1] = (IntegerArrayGenome) pop.getGenomeFactory().factor();
        for (int i = 1; i <= 10; i++) {
            parents[0].getData()[i - 1] = i;
            parents[1].getData()[i - 1] = 11 - i;
        }
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        // Create a random number generator
        GenerateRandom rnd = new MersenneTwisterGenerateRandom();

        // Create a new population.
        Population pop = new BasicPopulation();
        pop.setGenomeFactory(new IntegerArrayGenomeFactory(10));

        // 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;
            }
        });

        // Create a splice (no repeat) operator, length = 5.  Use it 1.0 (100%) of the time.
        SpliceNoRepeat opp = new SpliceNoRepeat(5);
        train.addOperation(1.0, opp);

        // Create two parents, the genes are set to 1,2,3,4,5,7,8,9,10
        // and 10,9,8,7,6,5,4,3,2,1.
        IntegerArrayGenome[] parents = new IntegerArrayGenome[2];
        parents[0] = (IntegerArrayGenome) pop.getGenomeFactory().factor();
        parents[1] = (IntegerArrayGenome) pop.getGenomeFactory().factor();
        for (int i = 1; i <= 10; i++) {
            parents[0].getData()[i - 1] = i;
            parents[1].getData()[i - 1] = 11 - i;
        }
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*/
public class TournamentCompareExample {
    public static void main(String[] args) {

        // Create a new population.
        Population pop = new BasicPopulation();
        Species species = pop.createSpecies();

        // Create 1000 genomes, assign the score to be the index number.
        for (int i = 0; i < 1000; i++) {
            Genome genome = new IntegerArrayGenome(1);
            genome.setScore(i);
            genome.setAdjustedScore(i);
            pop.getSpecies().get(0).add(genome);
        }

        GenerateRandom rnd = new MersenneTwisterGenerateRandom();

        // Create a trainer with a very simple score function.  We do not care
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        // Create a random number generator
        GenerateRandom rnd = new MersenneTwisterGenerateRandom();

        // Create a new population.
        Population pop = new BasicPopulation();
        pop.setGenomeFactory(new IntegerArrayGenomeFactory(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;
            }
        });

        // Create a shuffle operator.  Use it 1.0 (100%) of the time.
        MutateShuffle opp = new MutateShuffle();
        train.addOperation(1.0, opp);

        // Create a single parent, the genes are set to 1,2,3,4,5.
        IntegerArrayGenome[] parents = new IntegerArrayGenome[1];
        parents[0] = (IntegerArrayGenome) pop.getGenomeFactory().factor();
        for (int i = 1; i <= 5; i++) {
            parents[0].getData()[i - 1] = i;
        }

        // Create an array to hold the offspring.
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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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Related Classes of com.heatonresearch.aifh.evolutionary.population.BasicPopulation

  • com.heatonresearch.aifh.evolutionary.genome.Genome
  • com.heatonresearch.aifh.evolutionary.species.BasicSpecies
  • com.heatonresearch.aifh.evolutionary.species.Species
  • 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
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