Package org.encog.ml.ea.train.basic

Examples of org.encog.ml.ea.train.basic.TrainEA.addOperation()

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        new NEATMutateWeights(new SelectProportion(0.02),
            new MutateResetLinkWeight()));
    weightMutation.getComponents().finalizeStructure();

    result.setChampMutation(weightMutation);
    result.addOperation(0.5, new NEATCrossover());
    result.addOperation(0.494, weightMutation);
    result.addOperation(0.0005, new NEATMutateAddNode());
    result.addOperation(0.005, new NEATMutateAddLink());
    result.addOperation(0.0005, new NEATMutateRemoveLink());
    result.getOperators().finalizeStructure();
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            new MutateResetLinkWeight()));
    weightMutation.getComponents().finalizeStructure();

    result.setChampMutation(weightMutation);
    result.addOperation(0.5, new NEATCrossover());
    result.addOperation(0.494, weightMutation);
    result.addOperation(0.0005, new NEATMutateAddNode());
    result.addOperation(0.005, new NEATMutateAddLink());
    result.addOperation(0.0005, new NEATMutateRemoveLink());
    result.getOperators().finalizeStructure();
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    weightMutation.getComponents().finalizeStructure();

    result.setChampMutation(weightMutation);
    result.addOperation(0.5, new NEATCrossover());
    result.addOperation(0.494, weightMutation);
    result.addOperation(0.0005, new NEATMutateAddNode());
    result.addOperation(0.005, new NEATMutateAddLink());
    result.addOperation(0.0005, new NEATMutateRemoveLink());
    result.getOperators().finalizeStructure();

    if (population.isHyperNEAT()) {
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    result.setChampMutation(weightMutation);
    result.addOperation(0.5, new NEATCrossover());
    result.addOperation(0.494, weightMutation);
    result.addOperation(0.0005, new NEATMutateAddNode());
    result.addOperation(0.005, new NEATMutateAddLink());
    result.addOperation(0.0005, new NEATMutateRemoveLink());
    result.getOperators().finalizeStructure();

    if (population.isHyperNEAT()) {
      result.setCODEC(new HyperNEATCODEC());
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    result.setChampMutation(weightMutation);
    result.addOperation(0.5, new NEATCrossover());
    result.addOperation(0.494, weightMutation);
    result.addOperation(0.0005, new NEATMutateAddNode());
    result.addOperation(0.005, new NEATMutateAddLink());
    result.addOperation(0.0005, new NEATMutateRemoveLink());
    result.getOperators().finalizeStructure();

    if (population.isHyperNEAT()) {
      result.setCODEC(new HyperNEATCODEC());
    } else {
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    final CalculateScore score = new TrainingSetScore(training);   
    TrainEA train = new TrainEA(pop, score);
    train.getRules().addRewriteRule(new RewriteConstants());
    train.getRules().addRewriteRule(new RewriteAlgebraic());
    train.setCODEC(new PrgCODEC());
    train.addOperation(0.8, new SubtreeCrossover());
    train.addOperation(0.1, new SubtreeMutation(pop.getContext(),4));
    train.addOperation(0.1, new ConstMutation(pop.getContext(),0.5,1.0));
    train.addScoreAdjuster(new ComplexityAdjustedScore());
    train.setSpeciation(new PrgSpeciation());
    return train;
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    TrainEA train = new TrainEA(pop, score);
    train.getRules().addRewriteRule(new RewriteConstants());
    train.getRules().addRewriteRule(new RewriteAlgebraic());
    train.setCODEC(new PrgCODEC());
    train.addOperation(0.8, new SubtreeCrossover());
    train.addOperation(0.1, new SubtreeMutation(pop.getContext(),4));
    train.addOperation(0.1, new ConstMutation(pop.getContext(),0.5,1.0));
    train.addScoreAdjuster(new ComplexityAdjustedScore());
    train.setSpeciation(new PrgSpeciation());
    return train;
  }
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    train.getRules().addRewriteRule(new RewriteConstants());
    train.getRules().addRewriteRule(new RewriteAlgebraic());
    train.setCODEC(new PrgCODEC());
    train.addOperation(0.8, new SubtreeCrossover());
    train.addOperation(0.1, new SubtreeMutation(pop.getContext(),4));
    train.addOperation(0.1, new ConstMutation(pop.getContext(),0.5,1.0));
    train.addScoreAdjuster(new ComplexityAdjustedScore());
    train.setSpeciation(new PrgSpeciation());
    return train;
  }
}
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    CalculateScore score = new ZeroEvalScoreFunction();

    TrainEA genetic = new TrainEA(pop, score);
    genetic.setValidationMode(true);
    genetic.setCODEC(new PrgCODEC());
    genetic.addOperation(0.95, new SubtreeCrossover());
    genetic.addOperation(0.05, new SubtreeMutation(context,4));
    genetic.addScoreAdjuster(new ComplexityAdjustedScore());
    genetic.getRules().addRewriteRule(new RewriteConstants());
    genetic.getRules().addRewriteRule(new RewriteAlgebraic());
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    TrainEA genetic = new TrainEA(pop, score);
    genetic.setValidationMode(true);
    genetic.setCODEC(new PrgCODEC());
    genetic.addOperation(0.95, new SubtreeCrossover());
    genetic.addOperation(0.05, new SubtreeMutation(context,4));
    genetic.addScoreAdjuster(new ComplexityAdjustedScore());
    genetic.getRules().addRewriteRule(new RewriteConstants());
    genetic.getRules().addRewriteRule(new RewriteAlgebraic());

    EncogProgram expression = new EncogProgram(context);
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