Uses of Interface
org.cicirello.search.problems.OptimizationProblem

Packages that use OptimizationProblem

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.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.problems.tsp	Classes and interfaces related to the Traveling Salesperson Problem (TSP).
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 OptimizationProblem in org.cicirello.search.evo

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

Modifier and Type	Method	Description
OptimizationProblem<T>	NegativeCostFitnessFunction.getProblem()

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

Modifier	Constructor	Description
	InverseCostFitnessFunction(OptimizationProblem<T> problem)	Constructs a fitness function that transforms the cost of solution s to fitness with the following transformation: fitness(s) = 1.0 / (1.0 + problem.cost(s) - problem.minCost()).
	InverseCostFitnessFunction(OptimizationProblem<T> problem, double c)	Constructs a fitness function that transforms the cost of solution s to fitness with the following transformation: fitness(s) = c / (c + problem.cost(s) - problem.minCost()).
	NegativeCostFitnessFunction(OptimizationProblem<T> problem)	Constructs a fitness function that transforms the cost of solution s to fitness with the following transformation: fitness(s) = -cost(s).
	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.
	OnePlusOneGeneticAlgorithm(OptimizationProblem<BitVector> problem, double m, int bitLength)	Creates a OnePlusOneGeneticAlgorithm instance for real-valued optimization problems.
	OnePlusOneGeneticAlgorithm(OptimizationProblem<BitVector> problem, double m, int bitLength, ProgressTracker<BitVector> tracker)	Creates a OnePlusOneGeneticAlgorithm instance for real-valued optimization problems.

Uses of OptimizationProblem in org.cicirello.search.hc

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

Modifier	Constructor	Description
	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(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 OptimizationProblem in org.cicirello.search.problems

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

Modifier and Type	Class	Description
final class	CostFunctionScaler<T extends Copyable<T>>	This is a wrapper class for OptimizationProblem objects that enables scaling all cost values by a positive constant.
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].
final class	HollandRoyalRoad	Implementation of Holland's Royal Road problem, as described in the following paper: Terry Jones.
final class	Mix	This class implements Ackley's Mix problem, an artificial landscape that is a mix of the OneMax, TwoMax, Trap, and Plateau problems, which provides for a landscape that combines all of the properties of these benchmarking problems.
static final class	PermutationToBitVectorProblem.DoubleCost	This class implements a mapping between Permutation problems and BitVector problems, where cost values are of type double.
final class	Plateaus	This class implements Ackley's Plateaus problem, an artificial search landscape over the space of bitstrings that is characterized by large flat regions known as plateaus.
final class	PolynomialRootFinding	This class defines polynomial root finding as an optimization problem, enabling solving via simulated annealing or other metaheuristic optimization algorithms.
final class	Trap	This class implements Ackley's Trap function, which defines a fitness landscape with a single global optima, and a single sub-optimal local optima, such that most of the search landscape is within the attraction basin of the local optima.

Constructors in org.cicirello.search.problems with parameters of type OptimizationProblem

Modifier	Constructor	Description
	CostFunctionScaler(OptimizationProblem<T> problem, double scale)	Constructs the CostFunctionScaler.
	DoubleCost(OptimizationProblem<Permutation> problem, int permutationLength)	Initializes the mapping between Permutation problem and BitVector problem for a specific permutation length.

Uses of OptimizationProblem in org.cicirello.search.problems.tsp

Classes in org.cicirello.search.problems.tsp that implement OptimizationProblem

Modifier and Type	Class	Description
static final class	RandomTSPMatrix.Double	This class implements the Traveling Salesperson Problem (TSP), and its variant, the Asymmetric Traveling Salesperson Problem (ATSP), by generating a random distance matrix, with floating-point cost edges.
static final class	TSP.Double	Cost function for the Traveling Salesperson Problem (TSP), where edge costs are floating-point valued.
static final class	TSP.DoubleMatrix	Cost function for the Traveling Salesperson Problem (TSP), where edge costs are floating-point valued, and where all edge costs between pairs of cities are precomputed.

Uses of OptimizationProblem in org.cicirello.search.sa

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

Modifier	Constructor	Description
	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 OptimizationProblem in org.cicirello.search.ss

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

Modifier	Constructor	Description
	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.