Module io.jenetics.base

Package io.jenetics

Class ProbabilitySelector<G extends Gene<?,G>,C extends Comparable<? super C>>

java.lang.Object

io.jenetics.ProbabilitySelector<G,C>

All Implemented Interfaces:

Selector<G,C>

Direct Known Subclasses:

BoltzmannSelector, ExponentialRankSelector, LinearRankSelector, RouletteWheelSelector

public abstract class ProbabilitySelector<G extends Gene<?,G>,C extends Comparable<? super C>>
extends Object
implements Selector<G,C>

Probability selectors are a variation of fitness proportional selectors and selects individuals from a given population based on its selection probability P(i).

Fitness proportional selection works as shown in the figure above. The runtime complexity of the implemented probability selectors is O(n+log(n)) instead of O(n²) as for the naive approach: A binary (index) search is performed on the summed probability array.

Since:

1.0

Version:

4.0

Field Summary

Fields

Modifier and Type	Field	Description
protected final Function<double[],double[]>	_reverter
protected final boolean	_sorted
protected final Comparator<Phenotype<G,C>>	POPULATION_COMPARATOR

Constructor Summary

Constructors

Modifier	Constructor	Description
protected	ProbabilitySelector()	Create a new selector with sorted = false.
protected	ProbabilitySelector(boolean sorted)	Create a new ProbabilitySelector with the given sorting flag.

Method Summary

Modifier and Type	Method	Description
protected abstract double[]	probabilities(Seq<Phenotype<G,C>> population, int count)	Return an Probability array, which corresponds to the given Population.
protected final double[]	probabilities(Seq<Phenotype<G,C>> population, int count, Optimize opt)	This method takes the probabilities from the probabilities(Seq, int) method and inverts it if needed.
ISeq<Phenotype<G,C>>	select(Seq<Phenotype<G,C>> population, int count, Optimize opt)	Select phenotypes from the Population.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Details

POPULATION_COMPARATOR

protected final Comparator<Phenotype<G extends Gene<?,G>,C extends Comparable<? super C>>> POPULATION_COMPARATOR

_sorted

protected final boolean _sorted

_reverter

protected final Function<double[],double[]> _reverter

Constructor Details

ProbabilitySelector

protected ProbabilitySelector(boolean sorted)

Create a new ProbabilitySelector with the given sorting flag. This flag must set to true if the selector implementation is sorting the population in the probabilities(Seq, int) method.

Parameters:

sorted - true if the implementation is sorting the population when calculating the selection probabilities, false otherwise.

ProbabilitySelector

protected ProbabilitySelector()

Create a new selector with sorted = false.

Method Details

select

public ISeq<Phenotype<G,C>> select(Seq<Phenotype<G,C>> population,
 int count,
 Optimize opt)

Description copied from interface: Selector

Select phenotypes from the Population.

Specified by:

select in interface Selector<G extends Gene<?,G>,C extends Comparable<? super C>>

Parameters:

population - The population to select from.

count - The number of phenotypes to select.

opt - Determines whether the individuals with higher fitness values or lower fitness values must be selected. This parameter determines whether the GA maximizes or minimizes the fitness function.

Returns:

The selected phenotypes (a new Population).

probabilities

protected final double[] probabilities(Seq<Phenotype<G,C>> population,
 int count,
 Optimize opt)

This method takes the probabilities from the probabilities(Seq, int) method and inverts it if needed.

Parameters:

population - The population.

count - The number of phenotypes to select.

opt - Determines whether the individuals with higher fitness values or lower fitness values must be selected. This parameter determines whether the GA maximizes or minimizes the fitness function.

Returns:

Probability array.

probabilities

protected abstract double[] probabilities(Seq<Phenotype<G,C>> population,
 int count)

Return an Probability array, which corresponds to the given Population. The probability array and the population must have the same size. The population is not sorted. If a subclass needs a sorted population, the subclass is responsible to sort the population.

The implementer always assumes that higher fitness values are better. The base class inverts the probabilities, by reverting the returned probability array, if the GA is supposed to minimize the fitness function.

Parameters:

population - The unsorted population.

count - The number of phenotypes to select. This parameter is not needed for most implementations.

Returns:

Probability array. The returned probability array must have the length population.size() and must sum to one. The returned value is checked with assert(Math.abs(math.sum(probabilities) - 1.0) < 0.0001) in the base class.