EvolutionaryAlgorithm (Encog Core 3.3.0 API)

All Known Implementing Classes:

BasicEA, MLMethodGeneticAlgorithm.MLMethodGeneticAlgorithmHelper, TrainEA
```
public interface EvolutionaryAlgorithm
```
This interface defines the basic functionality of an Evolutionary Algorithm. An evolutionary algorithm is one that applies operations to a population of potential "solutions".

Method Summary

Methods
Modifier and Type	Method and Description
`void`	`addOperation(double probability, EvolutionaryOperator opp)` Add an operation.
`void`	`addScoreAdjuster(AdjustScore scoreAdjust)` Add a score adjuster.
`void`	`calculateScore(Genome g)` Calculate the score for a genome.
`void`	`finishTraining()` Called when training is finished.
`GenomeComparator`	`getBestComparator()` Get the comparator that is used to choose the "true best" genome.
`Genome`	`getBestGenome()`
`GeneticCODEC`	`getCODEC()`
`double`	`getError()`
`int`	`getIteration()`
`int`	`getMaxIndividualSize()`
`int`	`getMaxTries()`
`OperationList`	`getOperators()`
`Population`	`getPopulation()`
`RuleHolder`	`getRules()`
`List<AdjustScore>`	`getScoreAdjusters()`
`CalculateScore`	`getScoreFunction()`
`SelectionOperator`	`getSelection()`
`GenomeComparator`	`getSelectionComparator()` Get the comparator that is used to choose the "best" genome for selection, as opposed to the "true best".
`boolean`	`getShouldIgnoreExceptions()`
`Speciation`	`getSpeciation()`
`boolean`	`isValidationMode()`
`void`	`iteration()` Perform a training iteration.
`void`	`setBestComparator(GenomeComparator bestComparator)` Set the comparator that is used to choose the "true best" genome.
`void`	`setPopulation(Population thePopulation)` Set the population.
`void`	`setRules(RuleHolder rules)` Set the rules holder to use.
`void`	`setSelection(SelectionOperator selection)` Set the selection operator.
`void`	`setSelectionComparator(GenomeComparator selectionComparator)` Set the comparator that is used to choose the "best" genome for selection, as opposed to the "true best".
`void`	`setShouldIgnoreExceptions(boolean b)` Determines if genetic operator exceptions should be ignored.
`void`	`setSpeciation(Speciation m)` Set the speciation method.
`void`	`setValidationMode(boolean validationMode)` Determine if the genomes should be validated.

- Method Detail
  - addOperation
```
void addOperation(double probability,
                EvolutionaryOperator opp)
```
    Add an operation.
    
    Parameters:
    probability - The probability of using this operator.
    opp - The operator to add.
  - addScoreAdjuster
```
void addScoreAdjuster(AdjustScore scoreAdjust)
```
    Add a score adjuster. Score adjusters are used to adjust the adjusted score of a genome. This allows bonuses and penalties to be applied for desirable or undesirable traits.
    
    Parameters:
    scoreAdjust - The score adjustor to add.
  - calculateScore
```
void calculateScore(Genome g)
```
    Calculate the score for a genome.
    
    Parameters:
    g - The genome to calculate the score for.
  - finishTraining
```
void finishTraining()
```
    Called when training is finished. This allows the EA to properly shut down.
  - getBestComparator
```
GenomeComparator getBestComparator()
```
    Get the comparator that is used to choose the "true best" genome. This uses the real score, and not the adjusted score.
    
    Returns:
    The best comparator.
  - getBestGenome
```
Genome getBestGenome()
```
    Returns:
    The current best genome. This genome is safe to use while the EA is running. Genomes are not modified. They simply produce "offspring".
  - getCODEC
```
GeneticCODEC getCODEC()
```
    Returns:
    The CODEC that is used to transform between genome and phenome.
  - getError
```
double getError()
```
    Returns:
    The current score. This value should either be minimized or maximized, depending on the score function.
  - getIteration
```
int getIteration()
```
    Returns:
    The current iteration number. Also sometimes referred to as generation or epoch.
  - getMaxIndividualSize
```
int getMaxIndividualSize()
```
    Returns:
    The maximum size an individual genome can be. This is an arbitrary number defined by the genome. Lower numbers are less complex.
  - getMaxTries
```
int getMaxTries()
```
    Returns:
    The maximum number to try certain genetic operations. This prevents endless loops.
  - getOperators
```
OperationList getOperators()
```
    Returns:
    The operators.
  - getPopulation
```
Population getPopulation()
```
    Returns:
    The population.
  - getRules
```
RuleHolder getRules()
```
    Returns:
    The rules holder, contains rewrite and constraint rules.
  - getScoreAdjusters
```
List<AdjustScore> getScoreAdjusters()
```
    Returns:
    The score adjusters. This allows bonuses and penalties to be applied for desirable or undesirable traits.
  - getScoreFunction
```
CalculateScore getScoreFunction()
```
    Returns:
    The score function.
  - getSelection
```
SelectionOperator getSelection()
```
    Returns:
    The selection operator. Used to choose genomes.
  - getSelectionComparator
```
GenomeComparator getSelectionComparator()
```
    Get the comparator that is used to choose the "best" genome for selection, as opposed to the "true best". This uses the adjusted score, and not the score.
    
    Returns:
    The selection comparator.
  - getShouldIgnoreExceptions
```
boolean getShouldIgnoreExceptions()
```
    Returns:
    True if exceptions that occur during genetic operations should be ignored.
  - getSpeciation
```
Speciation getSpeciation()
```
    Returns:
    The speciation method.
  - isValidationMode
```
boolean isValidationMode()
```
    Returns:
    True if any genome validators should be applied.
  - iteration
```
void iteration()
```
    Perform a training iteration. Also called generations or epochs.
  - setBestComparator
```
void setBestComparator(GenomeComparator bestComparator)
```
    Set the comparator that is used to choose the "true best" genome. This uses the real score, and not the adjusted score.
    
    Parameters:
    bestComparator - The best comparator.
  - setPopulation
```
void setPopulation(Population thePopulation)
```
    Set the population.
    
    Parameters:
    thePopulation - The population.
  - setRules
```
void setRules(RuleHolder rules)
```
    Set the rules holder to use.
    
    Parameters:
    rules - The rules holder.
  - setSelection
```
void setSelection(SelectionOperator selection)
```
    Set the selection operator.
    
    Parameters:
    selection - The selection operator.
  - setSelectionComparator
```
void setSelectionComparator(GenomeComparator selectionComparator)
```
    Set the comparator that is used to choose the "best" genome for selection, as opposed to the "true best". This uses the adjusted score, and not the score.
    
    Parameters:
    selectionComparator - The selection comparator.
  - setShouldIgnoreExceptions
```
void setShouldIgnoreExceptions(boolean b)
```
    Determines if genetic operator exceptions should be ignored.
    
    Parameters:
    b - True if exceptions should be ignored.
  - setSpeciation
```
void setSpeciation(Speciation m)
```
    Set the speciation method.
    
    Parameters:
    m - The speciation method.
  - setValidationMode
```
void setValidationMode(boolean validationMode)
```
    Determine if the genomes should be validated. This takes more time but can help isolate a problem.
    
    Parameters:
    validationMode - True, if validation mode is enabled.

Interface EvolutionaryAlgorithm

Method Summary

Method Detail

addOperation

addScoreAdjuster

calculateScore

finishTraining

getBestComparator

getBestGenome

getCODEC

getError

getIteration

getMaxIndividualSize

getMaxTries

getOperators

getPopulation

getRules

getScoreAdjusters

getScoreFunction

getSelection

getSelectionComparator

getShouldIgnoreExceptions

getSpeciation

isValidationMode

iteration

setBestComparator

setPopulation

setRules

setSelection

setSelectionComparator

setShouldIgnoreExceptions

setSpeciation

setValidationMode