Uses of Interface org.cicirello.search.problems.OptimizationProblem (Chips-n-Salsa - A Java library of customizable, hybridizable, iterative, parallel, stochastic, and self-adaptive local search algorithms)

Packages that use OptimizationProblem
Package	Description
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.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.hc

Constructors in org.cicirello.search.hc with parameters of type OptimizationProblem
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
`class`	`CostFunctionScaler<T extends Copyable<T>>`	This is a wrapper class for `OptimizationProblem` objects that enables scaling all cost values by a positive constant.
`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].
`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`	`HollandRoyalRoad`	Implementation of Holland's Royal Road problem, as described in the following paper: Terry Jones.
`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.
`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.
`class`	`PolynomialRootFinding`	This class defines polynomial root finding as an optimization problem, enabling solving via simulated annealing or other metaheuristic optimization algorithms.
`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
Constructor	Description
`CostFunctionScaler(OptimizationProblem<T> problem, double scale)`	Constructs the CostFunctionScaler.

Uses of OptimizationProblem in org.cicirello.search.sa

Constructors in org.cicirello.search.sa with parameters of type OptimizationProblem
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 `ModifiedLam`, which is the Optimized Modified Lam of Cicirello (2020).
`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 `ModifiedLam`, which is the Optimized Modified Lam of Cicirello (2020).
`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 `ModifiedLam`, which is the Optimized Modified Lam of Cicirello (2020), 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 `ModifiedLam`, which is the Optimized Modified Lam of Cicirello (2020), 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
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.