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## org.encog.ml.genetic
Class MLMethodGeneticAlgorithm

    java.lang.Object
      org.encog.ml.train.BasicTraining
          org.encog.ml.genetic.MLMethodGeneticAlgorithm

All Implemented Interfaces:
  MLTrain, MultiThreadable

    public class MLMethodGeneticAlgorithm

extends BasicTraining
implements MultiThreadable

Implements a genetic algorithm that allows an MLMethod that is encodable (MLEncodable) to be trained. It works well with both BasicNetwork and FreeformNetwork class, as well as any MLEncodable class. There are essentially two ways you can make use of this class. Either way, you will need a score object. The score object tells the genetic algorithm how well suited a neural network is. If you would like to use genetic algorithms with a training set you should make use TrainingSetScore class. This score object uses a training set to score your neural network. If you would like to be more abstract, and not use a training set, you can create your own implementation of the CalculateScore method. This class can then score the networks any way that you like.

Nested Class Summary
`class` | `MLMethodGeneticAlgorithm.MLMethodGeneticAlgorithmHelper` Very simple class that implements a genetic algorithm.

Constructor Summary
`MLMethodGeneticAlgorithm(MethodFactory phenotypeFactory, CalculateScore calculateScore, int populationSize)` Construct a method genetic algorithm.

Method Summary
`boolean` | `canContinue()`
`MLMethodGeneticAlgorithm.MLMethodGeneticAlgorithmHelper` | `getGenetic()`
`MLMethod` | `getMethod()` Get the current best machine learning method from the training.
`int` | `getThreadCount()`
`void` | `iteration()` Perform one training iteration.
`TrainingContinuation` | `pause()` Pause the training to continue later.
`void` | `resume(TrainingContinuation state)` Resume training.
`void` | `setGenetic(MLMethodGeneticAlgorithm.MLMethodGeneticAlgorithmHelper genetic)` Set the genetic helper class.
`void` | `setThreadCount(int numThreads)` Set the number of threads to use.

Methods inherited from class org.encog.ml.train.BasicTraining
`addStrategy, finishTraining, getError, getImplementationType, getIteration, getStrategies, getTraining, isTrainingDone, iteration, postIteration, preIteration, setError, setIteration, setTraining`

Methods inherited from class java.lang.Object
`clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

Constructor Detail

### MLMethodGeneticAlgorithm

    public MLMethodGeneticAlgorithm(MethodFactory phenotypeFactory,
                                    CalculateScore calculateScore,
                                    int populationSize)

  Construct a method genetic algorithm.

  Parameters:
    `phenotypeFactory` - The phenotype factory.
    `calculateScore` - The score calculation object.
    `populationSize` - The population size.

Method Detail

### canContinue

    public boolean canContinue()

  Specified by:
    `canContinue` in interface `MLTrain`
  Returns:
    True if the training can be paused, and later continued.

### getGenetic

    public MLMethodGeneticAlgorithm.MLMethodGeneticAlgorithmHelper getGenetic()

  Returns:
    The genetic algorithm implementation.

### getMethod

    public MLMethod getMethod()

  Get the current best machine learning method from the training.

  Specified by:
    `getMethod` in interface `MLTrain`
  Returns:
    The best machine learningm method.

### getThreadCount

    public int getThreadCount()

  Specified by:
    `getThreadCount` in interface `MultiThreadable`
  Returns:
    The number of threads to use, 0 to automatically determine based on core count.

### iteration

    public void iteration()

  Perform one training iteration.

  Specified by:
    `iteration` in interface `MLTrain`

### pause

    public TrainingContinuation pause()

  Pause the training to continue later.

  Specified by:
    `pause` in interface `MLTrain`
  Returns:
    A training continuation object.

### resume

    public void resume(TrainingContinuation state)

  Resume training.

  Specified by:
    `resume` in interface `MLTrain`
  Parameters:
    `state` - The training continuation object to use to continue.

### setGenetic

    public void setGenetic(MLMethodGeneticAlgorithm.MLMethodGeneticAlgorithmHelper genetic)

  Set the genetic helper class.

  Parameters:
    `genetic` - The genetic helper class.

### setThreadCount

    public void setThreadCount(int numThreads)

  Description copied from interface: `MultiThreadable`
  Set the number of threads to use.

  Specified by:
    `setThreadCount` in interface `MultiThreadable`
  Parameters:
    `numThreads` - The number of threads to use, or zero to automatically determine based on core count.

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