Uses of Class
org.cicirello.search.ProgressTracker

Packages that use ProgressTracker

Package	Description
org.cicirello.search	This package includes classes and interfaces related to implementing metaheuristic search algorithms in general, rather than specific to a particular metaheuristic.
org.cicirello.search.concurrent	This package includes multithreaded search implementations, as well as classes and interfaces related to implementing multithreaded metaheuristics.
org.cicirello.search.evo	This package includes classes and interfaces directly related to implementing evolutionary algorithms.
org.cicirello.search.hc	This package includes classes and interfaces directly related to implementing hill climbers.
org.cicirello.search.restarts	This package includes classes and interfaces related to implementing multistart metaheuristics (i.e., metaheuristics that periodically restart, and return the best solution across a number of such restarts).
org.cicirello.search.sa	This package includes classes and interfaces directly related to implementing simulated annealing.
org.cicirello.search.ss	This package includes classes and interfaces directly related to implementing stochastic sampling algorithms.

Uses of ProgressTracker in org.cicirello.search

Methods in org.cicirello.search that return ProgressTracker

Modifier and Type	Method	Description
ProgressTracker<T>	TrackableSearch.getProgressTracker()	Gets the ProgressTracker object that is in use for tracking search progress.

Methods in org.cicirello.search with parameters of type ProgressTracker

Modifier and Type	Method	Description
void	TrackableSearch.setProgressTracker(ProgressTracker<T> tracker)	Sets the ProgressTracker object that is in use for tracking search progress.

Uses of ProgressTracker in org.cicirello.search.concurrent

Methods in org.cicirello.search.concurrent that return ProgressTracker

Modifier and Type	Method	Description
final ProgressTracker<T>	ParallelMetaheuristic.getProgressTracker()
final ProgressTracker<T>	TimedParallelMultistarter.getProgressTracker()

Methods in org.cicirello.search.concurrent with parameters of type ProgressTracker

Modifier and Type	Method	Description
final void	ParallelMetaheuristic.setProgressTracker(ProgressTracker<T> tracker)
final void	TimedParallelMultistarter.setProgressTracker(ProgressTracker<T> tracker)

Uses of ProgressTracker in org.cicirello.search.evo

Methods in org.cicirello.search.evo that return ProgressTracker

Modifier and Type	Method	Description
final ProgressTracker<T>	OnePlusOneEvolutionaryAlgorithm.getProgressTracker()

Methods in org.cicirello.search.evo with parameters of type ProgressTracker

Modifier and Type	Method	Description
final void	OnePlusOneEvolutionaryAlgorithm.setProgressTracker(ProgressTracker<T> tracker)

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

Modifier	Constructor	Description
	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, 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, 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, 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.
	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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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.
	OnePlusOneEvolutionaryAlgorithm(IntegerCostOptimizationProblem<T> problem, UndoableMutationOperator<T> mutation, Initializer<T> initializer, ProgressTracker<T> tracker)	Creates a OnePlusOneEvolutionaryAlgorithm instance for integer-valued optimization problems.
	OnePlusOneEvolutionaryAlgorithm(OptimizationProblem<T> problem, UndoableMutationOperator<T> mutation, Initializer<T> initializer, ProgressTracker<T> tracker)	Creates a OnePlusOneEvolutionaryAlgorithm instance for real-valued optimization problems.
	OnePlusOneGeneticAlgorithm(IntegerCostOptimizationProblem<BitVector> problem, double m, int bitLength, ProgressTracker<BitVector> tracker)	Creates a OnePlusOneGeneticAlgorithm instance for integer-valued optimization problems.
	OnePlusOneGeneticAlgorithm(OptimizationProblem<BitVector> problem, double m, int bitLength, ProgressTracker<BitVector> tracker)	Creates a OnePlusOneGeneticAlgorithm instance for real-valued optimization problems.
	SimpleGeneticAlgorithm(int n, int bitLength, FitnessFunction.Double<BitVector> f, double mutationRate, double crossoverRate, ProgressTracker<BitVector> tracker)	Initializes a simple genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation, single-point crossover (the SinglePointCrossover class), and fitness-proportional selection (the FitnessProportionalSelection class).
	SimpleGeneticAlgorithm(int n, int bitLength, FitnessFunction.Integer<BitVector> f, double mutationRate, double crossoverRate, ProgressTracker<BitVector> tracker)	Initializes a simple genetic algorithm with a generational model where children replace the parents, using the standard bit flip mutation, single-point crossover (the SinglePointCrossover class), and fitness-proportional selection (the FitnessProportionalSelection class).

Uses of ProgressTracker in org.cicirello.search.hc

Constructors in org.cicirello.search.hc with parameters of type ProgressTracker

Modifier	Constructor	Description
	FirstDescentHillClimber(IntegerCostOptimizationProblem<T> problem, IterableMutationOperator<T> mutation, Initializer<T> initializer, ProgressTracker<T> tracker)	Constructs a first descent hill climber object for integer-valued optimization problem.
	FirstDescentHillClimber(OptimizationProblem<T> problem, IterableMutationOperator<T> mutation, Initializer<T> initializer, ProgressTracker<T> tracker)	Constructs a first descent hill climber object for real-valued optimization problem.
	SteepestDescentHillClimber(IntegerCostOptimizationProblem<T> problem, IterableMutationOperator<T> mutation, Initializer<T> initializer, ProgressTracker<T> tracker)	Constructs a steepest descent hill climber object for integer-valued optimization problem.
	SteepestDescentHillClimber(OptimizationProblem<T> problem, IterableMutationOperator<T> mutation, Initializer<T> initializer, ProgressTracker<T> tracker)	Constructs a steepest descent hill climber object for real-valued optimization problem.

Uses of ProgressTracker in org.cicirello.search.restarts

Methods in org.cicirello.search.restarts that return ProgressTracker

Modifier and Type	Method	Description
final ProgressTracker<T>	Multistarter.getProgressTracker()

Methods in org.cicirello.search.restarts with parameters of type ProgressTracker

Modifier and Type	Method	Description
final void	Multistarter.setProgressTracker(ProgressTracker<T> tracker)

Uses of ProgressTracker in org.cicirello.search.sa

Methods in org.cicirello.search.sa that return ProgressTracker

Modifier and Type	Method	Description
final ProgressTracker<T>	SimulatedAnnealing.getProgressTracker()

Methods in org.cicirello.search.sa with parameters of type ProgressTracker

Modifier and Type	Method	Description
final void	SimulatedAnnealing.setProgressTracker(ProgressTracker<T> tracker)

Constructors in org.cicirello.search.sa with parameters of type ProgressTracker

Modifier	Constructor	Description
	SimulatedAnnealing(IntegerCostOptimizationProblem<T> problem, UndoableMutationOperator<T> mutation, Initializer<T> initializer, ProgressTracker<T> tracker)	Creates a SimulatedAnnealing search instance for integer-valued optimization problems, with a default annealing schedule of SelfTuningLam, which is the Self-Tuning Lam annealing schedule of Cicirello (2021).
	SimulatedAnnealing(IntegerCostOptimizationProblem<T> problem, UndoableMutationOperator<T> mutation, Initializer<T> initializer, ProgressTracker<T> tracker, SimpleLocalMetaheuristic<T> hc)	Creates a SimulatedAnnealing search instance for integer-valued optimization problems, with a default annealing schedule of SelfTuningLam, which is the Self-Tuning Lam annealing schedule of Cicirello (2021), and which runs a hill climber as a post-processing step.
	SimulatedAnnealing(IntegerCostOptimizationProblem<T> problem, UndoableMutationOperator<T> mutation, Initializer<T> initializer, AnnealingSchedule anneal, ProgressTracker<T> tracker)	Creates a SimulatedAnnealing search instance for integer-valued optimization problems.
	SimulatedAnnealing(IntegerCostOptimizationProblem<T> problem, UndoableMutationOperator<T> mutation, Initializer<T> initializer, AnnealingSchedule anneal, ProgressTracker<T> tracker, SimpleLocalMetaheuristic<T> hc)	Creates a SimulatedAnnealing search instance for integer-valued optimization problems that runs a hill climber as a post-processing step.
	SimulatedAnnealing(OptimizationProblem<T> problem, UndoableMutationOperator<T> mutation, Initializer<T> initializer, ProgressTracker<T> tracker)	Creates a SimulatedAnnealing search instance for real-valued optimization problems, with a default annealing schedule of SelfTuningLam, which is the Self-Tuning Lam annealing schedule of Cicirello (2021).
	SimulatedAnnealing(OptimizationProblem<T> problem, UndoableMutationOperator<T> mutation, Initializer<T> initializer, ProgressTracker<T> tracker, SimpleLocalMetaheuristic<T> hc)	Creates a SimulatedAnnealing search instance for real-valued optimization problems, with a default annealing schedule of SelfTuningLam, which is the Self-Tuning Lam annealing schedule of Cicirello (2021), and which runs a hill climber as a post-processing step.
	SimulatedAnnealing(OptimizationProblem<T> problem, UndoableMutationOperator<T> mutation, Initializer<T> initializer, AnnealingSchedule anneal, ProgressTracker<T> tracker)	Creates a SimulatedAnnealing search instance for real-valued optimization problems.
	SimulatedAnnealing(OptimizationProblem<T> problem, UndoableMutationOperator<T> mutation, Initializer<T> initializer, AnnealingSchedule anneal, ProgressTracker<T> tracker, SimpleLocalMetaheuristic<T> hc)	Creates a SimulatedAnnealing search instance for real-valued optimization problems that runs a hill climber as a post-processing step.

Uses of ProgressTracker in org.cicirello.search.ss

Methods in org.cicirello.search.ss that return ProgressTracker

Modifier and Type	Method	Description
final ProgressTracker<T>	HeuristicSolutionGenerator.getProgressTracker()

Methods in org.cicirello.search.ss with parameters of type ProgressTracker

Modifier and Type	Method	Description
static HeuristicPermutationGenerator	HeuristicPermutationGenerator.createHeuristicPermutationGenerator(ConstructiveHeuristic<Permutation> heuristic, ProgressTracker<Permutation> tracker)	Creates a HeuristicPermutationGenerator for generating solutions to an optimization problem using a constructive heuristic.
static <T extends Copyable<T>> HeuristicSolutionGenerator<T>	HeuristicSolutionGenerator.createHeuristicSolutionGenerator(ConstructiveHeuristic<T> heuristic, ProgressTracker<T> tracker)	Creates a HeuristicSolutionGenerator for generating solutions to an optimization problem using a constructive heuristic.
final void	HeuristicSolutionGenerator.setProgressTracker(ProgressTracker<T> tracker)

Constructors in org.cicirello.search.ss with parameters of type ProgressTracker

Modifier	Constructor	Description
	AcceptanceBandSampling(ConstructiveHeuristic<T> heuristic, double beta, ProgressTracker<T> tracker)	Constructs an AcceptanceBandSampling search object.
	AcceptanceBandSampling(ConstructiveHeuristic<T> heuristic, ProgressTracker<T> tracker)	Constructs an AcceptanceBandSampling search object.
	HeuristicBiasedStochasticSampling(ConstructiveHeuristic<T> heuristic, boolean exponentialBias, ProgressTracker<T> tracker)	Constructs a HeuristicBiasedStochasticSampling search object.
	HeuristicBiasedStochasticSampling(ConstructiveHeuristic<T> heuristic, double exponent, ProgressTracker<T> tracker)	Constructs a HeuristicBiasedStochasticSampling search object.
	HeuristicBiasedStochasticSampling(ConstructiveHeuristic<T> heuristic, ProgressTracker<T> tracker)	Constructs a HeuristicBiasedStochasticSampling search object.
	HeuristicBiasedStochasticSampling(ConstructiveHeuristic<T> heuristic, HeuristicBiasedStochasticSampling.BiasFunction bias, ProgressTracker<T> tracker)	Constructs a HeuristicBiasedStochasticSampling search object.
	IterativeSampling(IntegerCostOptimizationProblem<T> problem, Initializer<T> initializer, ProgressTracker<T> tracker)	Constructs an iterative sampling search for a integer-valued optimization problem.
	IterativeSampling(OptimizationProblem<T> problem, Initializer<T> initializer, ProgressTracker<T> tracker)	Constructs an iterative sampling search for a real-valued optimization problem.
	ValueBiasedStochasticSampling(ConstructiveHeuristic<T> heuristic, double exponent, ProgressTracker<T> tracker)	Constructs a ValueBiasedStochasticSampling search object.
	ValueBiasedStochasticSampling(ConstructiveHeuristic<T> heuristic, ProgressTracker<T> tracker)	Constructs a ValueBiasedStochasticSampling search object.
	ValueBiasedStochasticSampling(ConstructiveHeuristic<T> heuristic, ValueBiasedStochasticSampling.BiasFunction bias, ProgressTracker<T> tracker)	Constructs a ValueBiasedStochasticSampling search object.