}
}
private void performGenetic(ProjectEGFile file, MLDataSet trainingData) {
InputGenetic dialog = new InputGenetic();
if (dialog.process()) {
final int populationSize = dialog.getPopulationSize().getValue();
final double mutationPercent = dialog.getMutationPercent()
.getValue();
final double percentToMate = dialog.getPercentToMate().getValue();
CalculateScore score = new TrainingSetScore(trainingData);
final NeuralGeneticAlgorithm train = new NeuralGeneticAlgorithm(
(BasicNetwork) file.getObject(),
new RangeRandomizer(-1, 1), score, populationSize,
mutationPercent, percentToMate);
train.setTraining(trainingData);
startup(file, train, dialog.getMaxError().getValue() / 100.0);
}
}