Uses of Interface
org.cicirello.search.evo.SelectionOperator

Packages that use SelectionOperator

Package	Description
org.cicirello.search.evo	This package includes classes and interfaces directly related to implementing evolutionary algorithms.

Uses of SelectionOperator in org.cicirello.search.evo

Classes in org.cicirello.search.evo that implement SelectionOperator

Modifier and Type	Class	Description
class	BiasedFitnessProportionalSelection	This class implements a variation of fitness proportional selection that applies a bias function to transform the fitness values.
class	BiasedStochasticUniversalSampling	This class implements a variation of Stochastic Universal Sampling (SUS) that we call Biased Stochastic Universal Sampling (Biased SUS), which integrates the use of a bias function with SUS to enable transforming fitness values prior to the stochastic selection decisions.
final class	BoltzmannSelection	This class implements Boltzmann selection.
final class	BoltzmannStochasticUniversalSampling	This class implements Boltzmann selection using Stochastic Universal Sampling (SUS).
final class	ExponentialRankSelection	This class implements exponential rank selection.
final class	ExponentialRankStochasticUniversalSampling	This class implements exponential rank selection using Stochastic Universal Sampling (SUS).
class	FitnessProportionalSelection	This class implements fitness proportional selection, sometimes referred to as weighted roulette wheel, for evolutionary algorithms.
final class	FitnessShifter	FitnessShifter wraps another SelectionOperator, shifting all fitness values by the minimum fitness minus one, such that the least fit population member's transformed fitness is equal to 1, with the wrapped SelectionOperator than performing selection using the transformed fitnesses.
final class	LinearRankSelection	This class implements linear rank selection.
final class	LinearRankStochasticUniversalSampling	This class implements linear rank selection using Stochastic Universal Sampling (SUS).
final class	RandomSelection	This class implements a simple random selection operator that selects members of the population uniformly at random, independent of fitness values.
final class	SigmaScaling	Implements sigma scaling by wrapping your chosen selection operator.
class	StochasticUniversalSampling	This class implements Stochastic Universal Sampling (SUS), a selection operator for evolutionary algorithms.
final class	TournamentSelection	This class implements tournament selection for evolutionary algorithms.
final class	TruncationSelection	This class implements truncation selection for evolutionary algorithms.

Constructors in org.cicirello.search.evo with parameters of type SelectionOperator

Modifier	Constructor	Description
	AdaptiveEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, CrossoverOperator<T> crossover, Initializer<T> initializer, FitnessFunction.Double<T> f, SelectionOperator selection)	Constructs and initializes the evolutionary algorithm.
	AdaptiveEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, CrossoverOperator<T> crossover, Initializer<T> initializer, FitnessFunction.Double<T> f, SelectionOperator selection, int eliteCount)	Constructs and initializes the evolutionary algorithm.
	AdaptiveEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, CrossoverOperator<T> crossover, Initializer<T> initializer, FitnessFunction.Double<T> f, SelectionOperator selection, int eliteCount, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm.
	AdaptiveEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, CrossoverOperator<T> crossover, Initializer<T> initializer, FitnessFunction.Double<T> f, SelectionOperator selection, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm.
	AdaptiveEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, CrossoverOperator<T> crossover, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection)	Constructs and initializes the evolutionary algorithm.
	AdaptiveEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, CrossoverOperator<T> crossover, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, int eliteCount)	Constructs and initializes the evolutionary algorithm.
	AdaptiveEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, CrossoverOperator<T> crossover, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, int eliteCount, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm.
	AdaptiveEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, CrossoverOperator<T> crossover, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm.
	AdaptiveMutationOnlyEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, Initializer<T> initializer, FitnessFunction.Double<T> f, SelectionOperator selection)	Constructs and initializes the evolutionary algorithm.
	AdaptiveMutationOnlyEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, Initializer<T> initializer, FitnessFunction.Double<T> f, SelectionOperator selection, int eliteCount)	Constructs and initializes the evolutionary algorithm.
	AdaptiveMutationOnlyEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, Initializer<T> initializer, FitnessFunction.Double<T> f, SelectionOperator selection, int eliteCount, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm.
	AdaptiveMutationOnlyEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, Initializer<T> initializer, FitnessFunction.Double<T> f, SelectionOperator selection, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm.
	AdaptiveMutationOnlyEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection)	Constructs and initializes the evolutionary algorithm.
	AdaptiveMutationOnlyEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, int eliteCount)	Constructs and initializes the evolutionary algorithm.
	AdaptiveMutationOnlyEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, int eliteCount, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm.
	AdaptiveMutationOnlyEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm.
	FitnessShifter(SelectionOperator selection)	Constructs a new FitnessShifter object, to transform fitness values so that minumum fitness is 1.
	GenerationalEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, double mutationRate, CrossoverOperator<T> crossover, double crossoverRate, Initializer<T> initializer, FitnessFunction.Double<T> f, SelectionOperator selection)	Constructs and initializes the evolutionary algorithm.
	GenerationalEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, double mutationRate, CrossoverOperator<T> crossover, double crossoverRate, Initializer<T> initializer, FitnessFunction.Double<T> f, SelectionOperator selection, int eliteCount)	Constructs and initializes the evolutionary algorithm.
	GenerationalEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, double mutationRate, CrossoverOperator<T> crossover, double crossoverRate, Initializer<T> initializer, FitnessFunction.Double<T> f, SelectionOperator selection, int eliteCount, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm.
	GenerationalEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, double mutationRate, CrossoverOperator<T> crossover, double crossoverRate, Initializer<T> initializer, FitnessFunction.Double<T> f, SelectionOperator selection, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm.
	GenerationalEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, double mutationRate, CrossoverOperator<T> crossover, double crossoverRate, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection)	Constructs and initializes the evolutionary algorithm.
	GenerationalEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, double mutationRate, CrossoverOperator<T> crossover, double crossoverRate, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, int eliteCount)	Constructs and initializes the evolutionary algorithm.
	GenerationalEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, double mutationRate, CrossoverOperator<T> crossover, double crossoverRate, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, int eliteCount, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm.
	GenerationalEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, double mutationRate, CrossoverOperator<T> crossover, double crossoverRate, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm.
	GenerationalEvolutionaryAlgorithmMutuallyExclusiveOperators(int n, MutationOperator<T> mutation, double mutationRate, CrossoverOperator<T> crossover, double crossoverRate, Initializer<T> initializer, FitnessFunction.Double<T> f, SelectionOperator selection)	Constructs and initializes the evolutionary algorithm for an EA utilizing both a crossover operator and a mutation operator, such that the genetic operators follow a mutually exclusive property where each population member is involved in at most one of those operations in a single generation.
	GenerationalEvolutionaryAlgorithmMutuallyExclusiveOperators(int n, MutationOperator<T> mutation, double mutationRate, CrossoverOperator<T> crossover, double crossoverRate, Initializer<T> initializer, FitnessFunction.Double<T> f, SelectionOperator selection, int eliteCount)	Constructs and initializes the evolutionary algorithm for an EA utilizing both a crossover operator and a mutation operator, such that the genetic operators follow a mutually exclusive property where each population member is involved in at most one of those operations in a single generation.
	GenerationalEvolutionaryAlgorithmMutuallyExclusiveOperators(int n, MutationOperator<T> mutation, double mutationRate, CrossoverOperator<T> crossover, double crossoverRate, Initializer<T> initializer, FitnessFunction.Double<T> f, SelectionOperator selection, int eliteCount, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm for an EA utilizing both a crossover operator and a mutation operator, such that the genetic operators follow a mutually exclusive property where each population member is involved in at most one of those operations in a single generation.
	GenerationalEvolutionaryAlgorithmMutuallyExclusiveOperators(int n, MutationOperator<T> mutation, double mutationRate, CrossoverOperator<T> crossover, double crossoverRate, Initializer<T> initializer, FitnessFunction.Double<T> f, SelectionOperator selection, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm for an EA utilizing both a crossover operator and a mutation operator, such that the genetic operators follow a mutually exclusive property where each population member is involved in at most one of those operations in a single generation.
	GenerationalEvolutionaryAlgorithmMutuallyExclusiveOperators(int n, MutationOperator<T> mutation, double mutationRate, CrossoverOperator<T> crossover, double crossoverRate, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection)	Constructs and initializes the evolutionary algorithm for an EA utilizing both a crossover operator and a mutation operator, such that the genetic operators follow a mutually exclusive property where each population member is involved in at most one of those operations in a single generation.
	GenerationalEvolutionaryAlgorithmMutuallyExclusiveOperators(int n, MutationOperator<T> mutation, double mutationRate, CrossoverOperator<T> crossover, double crossoverRate, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, int eliteCount)	Constructs and initializes the evolutionary algorithm for an EA utilizing both a crossover operator and a mutation operator, such that the genetic operators follow a mutually exclusive property where each population member is involved in at most one of those operations in a single generation.
	GenerationalEvolutionaryAlgorithmMutuallyExclusiveOperators(int n, MutationOperator<T> mutation, double mutationRate, CrossoverOperator<T> crossover, double crossoverRate, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, int eliteCount, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm for an EA utilizing both a crossover operator and a mutation operator, such that the genetic operators follow a mutually exclusive property where each population member is involved in at most one of those operations in a single generation.
	GenerationalEvolutionaryAlgorithmMutuallyExclusiveOperators(int n, MutationOperator<T> mutation, double mutationRate, CrossoverOperator<T> crossover, double crossoverRate, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm for an EA utilizing both a crossover operator and a mutation operator, such that the genetic operators follow a mutually exclusive property where each population member is involved in at most one of those operations in a single generation.
	GenerationalMutationOnlyEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, double mutationRate, Initializer<T> initializer, FitnessFunction.Double<T> f, SelectionOperator selection)	Constructs and initializes the evolutionary algorithm with mutation only.
	GenerationalMutationOnlyEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, double mutationRate, Initializer<T> initializer, FitnessFunction.Double<T> f, SelectionOperator selection, int eliteCount)	Constructs and initializes the evolutionary algorithm with mutation only.
	GenerationalMutationOnlyEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, double mutationRate, Initializer<T> initializer, FitnessFunction.Double<T> f, SelectionOperator selection, int eliteCount, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm with mutation only.
	GenerationalMutationOnlyEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, double mutationRate, Initializer<T> initializer, FitnessFunction.Double<T> f, SelectionOperator selection, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm with mutation only.
	GenerationalMutationOnlyEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, double mutationRate, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection)	Constructs and initializes the evolutionary algorithm with mutation only.
	GenerationalMutationOnlyEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, double mutationRate, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, int eliteCount)	Constructs and initializes the evolutionary algorithm with mutation only.
	GenerationalMutationOnlyEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, double mutationRate, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, int eliteCount, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm with mutation only.
	GenerationalMutationOnlyEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, double mutationRate, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection, ProgressTracker<T> tracker)	Constructs and initializes the evolutionary algorithm with mutation only.
	GeneticAlgorithm(int n, int bitLength, FitnessFunction.Double<BitVector> f, double mutationRate, CrossoverOperator<BitVector> crossover, double crossoverRate, SelectionOperator selection)	Initializes a genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	GeneticAlgorithm(int n, int bitLength, FitnessFunction.Double<BitVector> f, double mutationRate, CrossoverOperator<BitVector> crossover, double crossoverRate, SelectionOperator selection, int eliteCount)	Initializes a genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	GeneticAlgorithm(int n, int bitLength, FitnessFunction.Double<BitVector> f, double mutationRate, CrossoverOperator<BitVector> crossover, double crossoverRate, SelectionOperator selection, int eliteCount, ProgressTracker<BitVector> tracker)	Initializes a genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	GeneticAlgorithm(int n, int bitLength, FitnessFunction.Double<BitVector> f, double mutationRate, CrossoverOperator<BitVector> crossover, double crossoverRate, SelectionOperator selection, ProgressTracker<BitVector> tracker)	Initializes a genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	GeneticAlgorithm(int n, int bitLength, FitnessFunction.Integer<BitVector> f, double mutationRate, CrossoverOperator<BitVector> crossover, double crossoverRate, SelectionOperator selection)	Initializes a genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	GeneticAlgorithm(int n, int bitLength, FitnessFunction.Integer<BitVector> f, double mutationRate, CrossoverOperator<BitVector> crossover, double crossoverRate, SelectionOperator selection, int eliteCount)	Initializes a genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	GeneticAlgorithm(int n, int bitLength, FitnessFunction.Integer<BitVector> f, double mutationRate, CrossoverOperator<BitVector> crossover, double crossoverRate, SelectionOperator selection, int eliteCount, ProgressTracker<BitVector> tracker)	Initializes a genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	GeneticAlgorithm(int n, int bitLength, FitnessFunction.Integer<BitVector> f, double mutationRate, CrossoverOperator<BitVector> crossover, double crossoverRate, SelectionOperator selection, ProgressTracker<BitVector> tracker)	Initializes a genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	GeneticAlgorithm(int n, Initializer<BitVector> initializer, FitnessFunction.Double<BitVector> f, double mutationRate, CrossoverOperator<BitVector> crossover, double crossoverRate, SelectionOperator selection)	Initializes a genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	GeneticAlgorithm(int n, Initializer<BitVector> initializer, FitnessFunction.Double<BitVector> f, double mutationRate, CrossoverOperator<BitVector> crossover, double crossoverRate, SelectionOperator selection, int eliteCount)	Initializes a genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	GeneticAlgorithm(int n, Initializer<BitVector> initializer, FitnessFunction.Double<BitVector> f, double mutationRate, CrossoverOperator<BitVector> crossover, double crossoverRate, SelectionOperator selection, int eliteCount, ProgressTracker<BitVector> tracker)	Initializes a genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	GeneticAlgorithm(int n, Initializer<BitVector> initializer, FitnessFunction.Double<BitVector> f, double mutationRate, CrossoverOperator<BitVector> crossover, double crossoverRate, SelectionOperator selection, ProgressTracker<BitVector> tracker)	Initializes a genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	GeneticAlgorithm(int n, Initializer<BitVector> initializer, FitnessFunction.Integer<BitVector> f, double mutationRate, CrossoverOperator<BitVector> crossover, double crossoverRate, SelectionOperator selection)	Initializes a genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	GeneticAlgorithm(int n, Initializer<BitVector> initializer, FitnessFunction.Integer<BitVector> f, double mutationRate, CrossoverOperator<BitVector> crossover, double crossoverRate, SelectionOperator selection, int eliteCount)	Initializes a genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	GeneticAlgorithm(int n, Initializer<BitVector> initializer, FitnessFunction.Integer<BitVector> f, double mutationRate, CrossoverOperator<BitVector> crossover, double crossoverRate, SelectionOperator selection, int eliteCount, ProgressTracker<BitVector> tracker)	Initializes a genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	GeneticAlgorithm(int n, Initializer<BitVector> initializer, FitnessFunction.Integer<BitVector> f, double mutationRate, CrossoverOperator<BitVector> crossover, double crossoverRate, SelectionOperator selection, ProgressTracker<BitVector> tracker)	Initializes a genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	MutationOnlyGeneticAlgorithm(int n, int bitLength, FitnessFunction.Double<BitVector> f, double mutationRate, SelectionOperator selection)	Initializes a mutation-only genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	MutationOnlyGeneticAlgorithm(int n, int bitLength, FitnessFunction.Double<BitVector> f, double mutationRate, SelectionOperator selection, int eliteCount)	Initializes a mutation-only genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	MutationOnlyGeneticAlgorithm(int n, int bitLength, FitnessFunction.Double<BitVector> f, double mutationRate, SelectionOperator selection, int eliteCount, ProgressTracker<BitVector> tracker)	Initializes a mutation-only genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	MutationOnlyGeneticAlgorithm(int n, int bitLength, FitnessFunction.Double<BitVector> f, double mutationRate, SelectionOperator selection, ProgressTracker<BitVector> tracker)	Initializes a mutation-only genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	MutationOnlyGeneticAlgorithm(int n, int bitLength, FitnessFunction.Integer<BitVector> f, double mutationRate, SelectionOperator selection)	Initializes a mutation-only genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	MutationOnlyGeneticAlgorithm(int n, int bitLength, FitnessFunction.Integer<BitVector> f, double mutationRate, SelectionOperator selection, int eliteCount)	Initializes a mutation-only genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	MutationOnlyGeneticAlgorithm(int n, int bitLength, FitnessFunction.Integer<BitVector> f, double mutationRate, SelectionOperator selection, int eliteCount, ProgressTracker<BitVector> tracker)	Initializes a mutation-only genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	MutationOnlyGeneticAlgorithm(int n, int bitLength, FitnessFunction.Integer<BitVector> f, double mutationRate, SelectionOperator selection, ProgressTracker<BitVector> tracker)	Initializes a mutation-only genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	MutationOnlyGeneticAlgorithm(int n, Initializer<BitVector> initializer, FitnessFunction.Double<BitVector> f, double mutationRate, SelectionOperator selection)	Initializes a mutation-only genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	MutationOnlyGeneticAlgorithm(int n, Initializer<BitVector> initializer, FitnessFunction.Double<BitVector> f, double mutationRate, SelectionOperator selection, int eliteCount)	Initializes a mutation-only genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	MutationOnlyGeneticAlgorithm(int n, Initializer<BitVector> initializer, FitnessFunction.Double<BitVector> f, double mutationRate, SelectionOperator selection, int eliteCount, ProgressTracker<BitVector> tracker)	Initializes a mutation-only genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	MutationOnlyGeneticAlgorithm(int n, Initializer<BitVector> initializer, FitnessFunction.Double<BitVector> f, double mutationRate, SelectionOperator selection, ProgressTracker<BitVector> tracker)	Initializes a mutation-only genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	MutationOnlyGeneticAlgorithm(int n, Initializer<BitVector> initializer, FitnessFunction.Integer<BitVector> f, double mutationRate, SelectionOperator selection)	Initializes a mutation-only genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	MutationOnlyGeneticAlgorithm(int n, Initializer<BitVector> initializer, FitnessFunction.Integer<BitVector> f, double mutationRate, SelectionOperator selection, int eliteCount)	Initializes a mutation-only genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	MutationOnlyGeneticAlgorithm(int n, Initializer<BitVector> initializer, FitnessFunction.Integer<BitVector> f, double mutationRate, SelectionOperator selection, int eliteCount, ProgressTracker<BitVector> tracker)	Initializes a mutation-only genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	MutationOnlyGeneticAlgorithm(int n, Initializer<BitVector> initializer, FitnessFunction.Integer<BitVector> f, double mutationRate, SelectionOperator selection, ProgressTracker<BitVector> tracker)	Initializes a mutation-only genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation.
	NaiveGenerationalEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, double mutationRate, CrossoverOperator<T> crossover, double crossoverRate, Initializer<T> initializer, FitnessFunction.Double<T> f, SelectionOperator selection)	Deprecated. Constructs and initializes the evolutionary algorithm.
	NaiveGenerationalEvolutionaryAlgorithm(int n, MutationOperator<T> mutation, double mutationRate, CrossoverOperator<T> crossover, double crossoverRate, Initializer<T> initializer, FitnessFunction.Integer<T> f, SelectionOperator selection)	Deprecated. Constructs and initializes the evolutionary algorithm.
	SigmaScaling(SelectionOperator selection)	Constructs a new SigmaScaling object, to transform fitness values via sigma scaling prior to selection by a wrapped selection operator.
	SigmaScaling(SelectionOperator selection, double c)	Constructs a new SigmaScaling object, to transform fitness values via sigma scaling prior to selection by a wrapped selection operator.