public class SigmaScalingSelection extends FitProportionateSelection
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 <= fitnessFloor.) |
Default Base
select.sigma-scaling
Modifier and Type | Field and Description |
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static java.lang.String |
P_SCALED_FITNESS_FLOOR
Scaled fitness floor
|
static java.lang.String |
P_SIGMA_SCALING
Default base
|
fitnesses, P_FITNESSPROPORTIONATE
INDS_PRODUCED, KEY_PARENTS
NO_PROBABILITY, P_PROB, probability
Constructor and Description |
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SigmaScalingSelection() |
Modifier and Type | Method and 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.
|
finishProducing, produce
produce, produces, produceWithoutCloning, typicalIndsProduced
clone, fillStubs, getProbability, pickRandom, preparePipeline, setProbability, setupProbabilities
public static final java.lang.String P_SIGMA_SCALING
public static final java.lang.String P_SCALED_FITNESS_FLOOR
public Parameter defaultBase()
Prototype
defaultBase
in interface Prototype
defaultBase
in class FitProportionateSelection
public void setup(EvolutionState state, Parameter base)
BreedingSource
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}.
setup
in interface Prototype
setup
in interface Setup
setup
in class BreedingSource
Prototype.setup(EvolutionState,Parameter)
public void prepareToProduce(EvolutionState s, int subpopulation, int thread)
SelectionMethod
prepareToProduce
in class FitProportionateSelection