Uses of Interface
org.cicirello.search.operators.Initializer

Packages that use Initializer

Package	Description
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.operators	This package includes classes and interfaces for defining various operators required by simulated annealing and other metaheuristics, such as mutation operators, along with other related classes and interfaces.
org.cicirello.search.operators.bits	This package includes classes that implement operators that create, mutate, etc, BitVectors.
org.cicirello.search.operators.integers	This package includes classes that implement operators that create, mutate, etc, integer valued representations.
org.cicirello.search.operators.permutations	This package includes classes that implement local search and evolutionary operators for permutations, such as mutation operators, crossover operators, and initializers.
org.cicirello.search.operators.reals	This package includes classes that implement operators that create, mutate, etc, the the inputs to functions with real-valued input parameters (represented with type double), such as is required to solve function optimization problems using simulated annealing or other metaheuristics.
org.cicirello.search.problems	Package of classes and interfaces related to representing computational problems, as well as classes implementing a variety of specific computational problems.
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 Initializer in org.cicirello.search.evo

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

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.
	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, 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, 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.
	OnePlusOneEvolutionaryAlgorithm(IntegerCostOptimizationProblem<T> problem, UndoableMutationOperator<T> mutation, Initializer<T> initializer)	Creates a OnePlusOneEvolutionaryAlgorithm instance for integer-valued optimization problems.
	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)	Creates a OnePlusOneEvolutionaryAlgorithm instance for real-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.

Uses of Initializer in org.cicirello.search.hc

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

Modifier	Constructor	Description
	FirstDescentHillClimber(IntegerCostOptimizationProblem<T> problem, IterableMutationOperator<T> mutation, Initializer<T> initializer)	Constructs a first descent hill climber object for integer-valued optimization problem.
	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)	Constructs a first descent hill climber object for real-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)	Constructs a steepest descent hill climber object for integer-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)	Constructs a steepest descent hill climber object for real-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 Initializer in org.cicirello.search.operators

Classes in org.cicirello.search.operators that implement Initializer

Modifier and Type	Class	Description
final class	InitializeBySimpleMetaheuristic<T extends Copyable<T>>	This class implements the Initializer interface to provide metaheuristics and other search algorithms with a way to generate initial candidate solutions to a problem, that are themselves generated via a metaheuristic.

Uses of Initializer in org.cicirello.search.operators.bits

Classes in org.cicirello.search.operators.bits that implement Initializer

Modifier and Type	Class	Description
final class	BitVectorInitializer	Generates random BitVector objects for use in generating random initial solutions for simulated annealing and other metaheuristics.

Uses of Initializer in org.cicirello.search.operators.integers

Classes in org.cicirello.search.operators.integers that implement Initializer

Modifier and Type	Class	Description
final class	IntegerValueInitializer	Generates random SingleInteger objects for use in generating random initial solutions for simulated annealing and other metaheuristics, and for copying such objects.
class	IntegerVectorInitializer	Generating random IntegerVector objects for use in generating random initial solutions for simulated annealing and other metaheuristics, and for copying such objects.

Uses of Initializer in org.cicirello.search.operators.permutations

Classes in org.cicirello.search.operators.permutations that implement Initializer

Modifier and Type	Class	Description
final class	PermutationInitializer	The PermutationInitializer provides metaheuristic implementations, such as of simulated annealing, etc, with a way to generate random initial solutions to a problem, as well as a way to make copies of current solution configurations.

Uses of Initializer in org.cicirello.search.operators.reals

Classes in org.cicirello.search.operators.reals that implement Initializer

Modifier and Type	Class	Description
final class	RealValueInitializer	Generating random SingleReal objects for use in generating random initial solutions for simulated annealing and other metaheuristics, and for copying such objects.
final class	RealVectorInitializer	Generates random RealVector objects for use in generating random initial solutions for simulated annealing and other metaheuristics, and for copying such objects.

Uses of Initializer in org.cicirello.search.problems

Classes in org.cicirello.search.problems that implement Initializer

Modifier and Type	Class	Description
final class	BoundMax	The BoundMax class is an implementation of a generalization of the well-known OneMax problem, often used in benchmarking genetic algorithms and other metaheuristics.
final class	ForresterEtAl2008	A continuous function with a single suboptimal local minimum, and a single global minimum, and a 0 derivative inflexion point, defined for inputs x in [0.0, 1.0].
final class	GramacyLee2012	A continuous function with a large number of local minimums, and a single global minimum, defined for input x in [0.5, 2.5].
class	PermutationToBitVectorProblem	This class implements a mapping between Permutation problems and BitVector problems, enabling using BitVector search operators to solve problems defined over the space of Permutation objects.
static final class	PermutationToBitVectorProblem.DoubleCost	This class implements a mapping between Permutation problems and BitVector problems, where cost values are of type double.
static final class	PermutationToBitVectorProblem.IntegerCost	This class implements a mapping between Permutation problems and BitVector problems, where cost values are of type int.

Uses of Initializer in org.cicirello.search.sa

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

Modifier	Constructor	Description
	SimulatedAnnealing(IntegerCostOptimizationProblem<T> problem, UndoableMutationOperator<T> mutation, Initializer<T> initializer)	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)	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)	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)	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(IntegerCostOptimizationProblem<T> problem, UndoableMutationOperator<T> mutation, Initializer<T> initializer, AnnealingSchedule anneal, SimpleLocalMetaheuristic<T> hc)	Creates a SimulatedAnnealing search instance for integer-valued optimization problems that runs a hill climber as a post-processing step.
	SimulatedAnnealing(IntegerCostOptimizationProblem<T> problem, UndoableMutationOperator<T> mutation, Initializer<T> initializer, 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(OptimizationProblem<T> problem, UndoableMutationOperator<T> mutation, Initializer<T> initializer)	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)	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)	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)	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.
	SimulatedAnnealing(OptimizationProblem<T> problem, UndoableMutationOperator<T> mutation, Initializer<T> initializer, AnnealingSchedule anneal, SimpleLocalMetaheuristic<T> hc)	Creates a SimulatedAnnealing search instance for real-valued optimization problems that runs a hill climber as a post-processing step.
	SimulatedAnnealing(OptimizationProblem<T> problem, UndoableMutationOperator<T> mutation, Initializer<T> initializer, 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.

Uses of Initializer in org.cicirello.search.ss

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

Modifier	Constructor	Description
	IterativeSampling(IntegerCostOptimizationProblem<T> problem, Initializer<T> initializer)	Constructs an iterative sampling search for a integer-valued optimization problem.
	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)	Constructs an iterative sampling search for a real-valued optimization problem.
	IterativeSampling(OptimizationProblem<T> problem, Initializer<T> initializer, ProgressTracker<T> tracker)	Constructs an iterative sampling search for a real-valued optimization problem.