public class SigmaScaling extends Object implements SelectionStrategy<Object>
RouletteWheelSelection
and StochasticUniversalSampling
. Uses the
mean population fitness and fitness standard deviation to adjust individual fitness
scores. Early on in an evolutionary algorithm this helps to avoid premature convergence
caused by the dominance of one or two relatively fit candidates in a population of mostly
unfit individuals. It also helps to amplify minor fitness differences in a more mature
population where the rate of improvement has slowed.Constructor and Description |
---|
SigmaScaling()
Creates a default sigma-scaled selection strategy.
|
SigmaScaling(SelectionStrategy<Object> delegate)
Creates a sigma-scaled selection strategy that delegates to the specified selection
strategy after adjusting individual fitness scores using sigma-scaling.
|
Modifier and Type | Method and Description |
---|---|
<S> List<S> |
select(List<EvaluatedCandidate<S>> population,
boolean naturalFitnessScores,
int selectionSize,
Random rng)
Select the specified number of candidates from the population.
|
String |
toString() |
public SigmaScaling()
public SigmaScaling(SelectionStrategy<Object> delegate)
delegate
- The proportionate selector that will be delegated
to after fitness scores have been adjusted using sigma-scaling.public <S> List<S> select(List<EvaluatedCandidate<S>> population, boolean naturalFitnessScores, int selectionSize, Random rng)
Select the specified number of candidates from the population. Implementations may assume that the population is sorted in descending order according to fitness (so the fittest individual is the first item in the list).
It is an error to call this method with an empty or null population.
select
in interface SelectionStrategy<Object>
S
- The type of evolved entity that we are selecting, a sub-type of T.population
- The population from which to select.naturalFitnessScores
- Whether higher fitness values represent fitter
individuals or not.selectionSize
- The number of individual selections to make (not necessarily
the number of distinct candidates to select, since the same individual may
potentially be selected more than once).rng
- Source of randomness for stochastic selection strategies.