org.encog.neural.networks.training.genetic
Class NeuralGeneticAlgorithm

java.lang.Object
  org.encog.ml.train.BasicTraining
      org.encog.neural.networks.training.genetic.NeuralGeneticAlgorithm

All Implemented Interfaces:

MLTrain, MultiThreadable

public class NeuralGeneticAlgorithm
extends BasicTraining
implements MultiThreadable

Implements a genetic algorithm that allows a feedforward or simple recurrent neural network to be trained using a genetic algorithm. 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

NeuralGeneticAlgorithm.NeuralGeneticAlgorithmHelper Very simple class that implements a genetic algorithm.

Constructor Summary

NeuralGeneticAlgorithm(BasicNetwork network, Randomizer randomizer, CalculateScore calculateScore, int populationSize, double mutationPercent, double percentToMate)
Construct a neural genetic algorithm.

Method Summary

boolean	canContinue()
NeuralGeneticAlgorithm.NeuralGeneticAlgorithmHelper	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(NeuralGeneticAlgorithm.NeuralGeneticAlgorithmHelper 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

NeuralGeneticAlgorithm

public NeuralGeneticAlgorithm(BasicNetwork network,
                              Randomizer randomizer,
                              CalculateScore calculateScore,
                              int populationSize,
                              double mutationPercent,
                              double percentToMate)

Construct a neural genetic algorithm.

Parameters:

network - The network to base this on.

randomizer - The randomizer used to create this initial population.

calculateScore - The score calculation object.

populationSize - The population size.

mutationPercent - The percent of offspring to mutate.

percentToMate - The percent of the population allowed to mate.

Method Detail

canContinue

public final boolean canContinue()

Specified by:

canContinue in interface MLTrain

Returns:

True if the training can be paused, and later continued.

getGenetic

public final NeuralGeneticAlgorithm.NeuralGeneticAlgorithmHelper getGenetic()

Returns:

The genetic algorithm implementation.

getMethod

public final MLMethod getMethod()

Get the current best machine learning method from the training.

Specified by:

getMethod in interface MLTrain

Returns:

The best machine learningm method.

iteration

public final void iteration()

Perform one training iteration.

Specified by:

iteration in interface MLTrain

pause

public final TrainingContinuation pause()

Pause the training to continue later.

Specified by:

pause in interface MLTrain

Returns:

A training continuation object.

resume

public final void resume(TrainingContinuation state)

Resume training.

Specified by:

resume in interface MLTrain

Parameters:

state - The training continuation object to use to continue.

setGenetic

public final void setGenetic(NeuralGeneticAlgorithm.NeuralGeneticAlgorithmHelper genetic)

Set the genetic helper class.

Parameters:

genetic - The genetic helper class.

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.

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.