Package ec.select

Class SigmaScalingSelection

java.lang.Object

ec.BreedingSource

ec.SelectionMethod

ec.select.FitProportionateSelection

ec.select.SigmaScalingSelection

All Implemented Interfaces:

Prototype, Setup, RandomChoiceChooserD, Serializable, Cloneable

public class SigmaScalingSelection
extends FitProportionateSelection

Similar to FitProportionateSelection, but with adjustments to scale up/exaggerate differences in fitness for selection when true fitness values are very close to eachother across the population. This addreses a common problem with FitProportionateSelection wherein selection approaches random selection during late runs when fitness values do not differ by much.

Like FitProportionateSelection this is not appropriate for steady-state evolution. If you're not familiar with the relative advantages of selection methods and just want a good one, use TournamentSelection instead. Not appropriate for multiobjective fitnesses.

Note: Fitnesses must be non-negative. 0 is assumed to be the worst fitness.

Typical Number of Individuals Produced Per produce(...) call
Always 1.

Parameters

base.scaled-fitness-floor double = some small number (defaults to 0.1)

(The sigma scaling formula sometimes returns negative values. This is unacceptable for fitness proportionate style selection so we must substitute the fitnessFloor (some value >= 0) for the sigma scaled fitness when that sigma scaled fitness invalid input: '<'= fitnessFloor.)

Default Base
select.sigma-scaling

See Also:

• Serialized Form

Field Summary

Fields

Modifier and Type	Field	Description
static final String	P_SCALED_FITNESS_FLOOR	Scaled fitness floor
static final String	P_SIGMA_SCALING	Default base

Fields inherited from class ec.select.FitProportionateSelection

fitnesses, P_FITNESSPROPORTIONATE

Fields inherited from class ec.SelectionMethod

INDS_PRODUCED, KEY_PARENTS

Fields inherited from class ec.BreedingSource

NO_PROBABILITY, P_PROB, probability

Constructor Summary

Constructors

Constructor	Description
SigmaScalingSelection()

Method Summary

Modifier and Type	Method	Description
Parameter	defaultBase()	Returns the default base for this prototype.
void	prepareToProduce(EvolutionState s, int subpopulation, int thread)	A default version of prepareToProduce which does nothing.
void	setup(EvolutionState state, Parameter base)	Sets up the BreedingPipeline.

Methods inherited from class ec.select.FitProportionateSelection

finishProducing, produce

Methods inherited from class ec.SelectionMethod

produce, produces, produceWithoutCloning, typicalIndsProduced

Methods inherited from class ec.BreedingSource

clone, fillStubs, getProbability, pickRandom, preparePipeline, setProbability, setupProbabilities

Methods inherited from class java.lang.Object

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

Field Details

P_SIGMA_SCALING

public static final String P_SIGMA_SCALING

Default base

See Also:

• Constant Field Values

P_SCALED_FITNESS_FLOOR

public static final String P_SCALED_FITNESS_FLOOR

Scaled fitness floor

See Also:

• Constant Field Values

Constructor Details

SigmaScalingSelection

public SigmaScalingSelection()

Method Details

defaultBase

public Parameter defaultBase()

Description copied from interface: Prototype

Returns the default base for this prototype. This should generally be implemented by building off of the static base() method on the DefaultsForm object for the prototype's package. This should be callable during setup(...).

Specified by:

defaultBase in interface Prototype

Overrides:

defaultBase in class FitProportionateSelection

setup

public void setup(EvolutionState state,
 Parameter base)

Description copied from class: BreedingSource

Sets up the BreedingPipeline. You can use state.output.error here because the top-level caller promises to call exitIfErrors() after calling setup. Note that probability might get modified again by an external source if it doesn't normalize right.

The most common modification is to normalize it with some other set of probabilities, then set all of them up in increasing summation; this allows the use of the fast static BreedingSource-picking utility method, BreedingSource.pickRandom(...). In order to use this method, for example, if four breeding source probabilities are {0.3, 0.2, 0.1, 0.4}, then they should get normalized and summed by the outside owners as: {0.3, 0.5, 0.6, 1.0}.

Specified by:

setup in interface Prototype

Specified by:

setup in interface Setup

Overrides:

setup in class BreedingSource

See Also:

• Prototype.setup(EvolutionState,Parameter)

prepareToProduce

public void prepareToProduce(EvolutionState s,
 int subpopulation,
 int thread)

Description copied from class: SelectionMethod

A default version of prepareToProduce which does nothing.

Overrides:

prepareToProduce in class FitProportionateSelection