public final class ExponentialRankSelector<G extends Gene<?,G>,C extends Comparable<? super C>> extends ProbabilitySelector<G,C>
An alternative to the "weak" LinearRankSelector
is to assign
survival probabilities to the sorted individuals using an exponential
function.
,
where c must within the range[0..1)
.
A small value of c increases the probability of the best phenotypes to be selected. If c is set to zero, the selection probability of the best phenotype is set to one. The selection probability of all other phenotypes is zero. A value near one equalizes the selection probabilities.
This selector sorts the population in descending order while calculating the selection probabilities.
_reverter, _sorted, POPULATION_COMPARATOR
Constructor and Description |
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ExponentialRankSelector()
Create a new selector with default value of 0.975.
|
ExponentialRankSelector(double c)
Create a new exponential rank selector.
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Modifier and Type | Method and Description |
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protected double[] |
probabilities(Seq<Phenotype<G,C>> population,
int count)
This method sorts the population in descending order while calculating the
selection probabilities.
|
String |
toString() |
probabilities, select
public ExponentialRankSelector(double c)
c
- the c value.IllegalArgumentException
- if c
is not within the range
[0..1)
.public ExponentialRankSelector()
protected double[] probabilities(Seq<Phenotype<G,C>> population, int count)
probabilities
in class ProbabilitySelector<G extends Gene<?,G>,C extends Comparable<? super C>>
population
- The unsorted population.count
- The number of phenotypes to select. This parameter is not
needed for most implementations.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.© 2007-2019 Franz Wilhelmstötter (2019-06-23 15:39)